Method and system for automated multidimensional assessment generation and delivery

ABSTRACT

Systems and methods for automated content generation and delivery are provided herein. The system can include a memory that can include a content item library. The content library can include a hierarchical data structure having levels and a plurality of data packets, each of which data packets is linked with at least a portion of the hierarchical data structure. The system can include at least one server that can generate an assessment creation interface including a plurality of nested objects each representative of a portion of the hierarchical data structure, receive a selection of a first object and a second object from the plurality of nested objects of the assessment creation interface, generate a weighting value for each of the selected objects, and generate an assessment from data packets associated with the selected objects according to the weighting value.

BACKGROUND

A computer network or data network is a telecommunications network whichallows computers to exchange data. In computer networks, networkedcomputing devices exchange data with each other along network links(data connections). The connections between nodes are established usingeither cable media or wireless media. The best-known computer network isthe Internet.

Network computer devices that originate, route and terminate the dataare called network nodes. Nodes can include hosts such as personalcomputers, phones, servers as well as networking hardware. Two suchdevices can be said to be networked together when one device is able toexchange information with the other device, whether or not they have adirect connection to each other.

Computer networks differ in the transmission media used to carry theirsignals, the communications protocols to organize network traffic, thenetwork's size, topology and organizational intent. In most cases,communications protocols are layered on (i.e. work using) other morespecific or more general communications protocols, except for thephysical layer that directly deals with the transmission media.

SUMMARY

One aspect of the present disclosure relates to a system for automatedassessment generation. The system includes a memory including a contentitem library containing a hierarchical data structure having levels anda plurality of data packets, each of which data packets is linked withat least a portion of the hierarchical data structure. The systemincludes at least one server. The at least one server can: generate anassessment creation interface, the assessment creation interfaceincluding a plurality of nested objects each representative of a portionof the hierarchical data structure; receive a selection of a firstobject and a second object from the plurality of nested objects of theassessment creation interface; generate a weighting value for each ofthe selected objects, which weighting value of an object identifies arelative contribution of the object to a level in the hierarchical datastructure; and generate an assessment from data packets associated withthe selected objects according to the weighting value.

In some embodiments, the assessment creation interface includes: a firstplurality of objects, each object of the first plurality of objectscorresponding to one of a plurality of domains; a second plurality ofobjects, each object of which second plurality of objects correspondingto one of a plurality of clusters; and a third plurality of objects,each object of which third plurality of objects corresponding to one ofa plurality of standards. In some embodiments, each of the firstplurality of objects includes a first-object boundary and contains atleast one of the second plurality of objects nested within thefirst-object boundary. In some embodiments, each of the second pluralityof objects includes a second object boundary and contains at least oneof the third plurality of objects nested within the second objectboundary.

In some embodiments, at least one of the first plurality of objectscontains some of the second plurality of objects nested within thefirst-object boundary. In some embodiments, at least one of the secondplurality of objects contains some of the third plurality of objectsnested within the second object boundary. In some embodiments, the atleast one server can generate a confirmation interface in response toreceipt of selection of the first object and the second object. In someembodiments, each of the first object and the second object includes oneof the third plurality of objects. In some embodiments, generating aweighting value includes: retrieving a raw weighting value for each ofthe first object and the second object; and generating a normalizedweighting value for each of the first object and the second object. Insome embodiments, generating the normalized weighting value includes:identifying a common level in the hierarchical structure, which commonlevel includes an object upstream coupled with each of the first objectand the second object. In some embodiments, the normalized weightingvalue is generated based on the common level.

One aspect of the present disclosure relates to a method for automatedassessment generation. The method includes: generating an assessmentcreation interface, the assessment creation interface including aplurality of nested objects each representative of a portion of ahierarchical data structure including levels and a plurality of datapackets, each of which data packets is linked with at least a portion ofthe hierarchical data structure; and receiving a selection of a firstobject and a second object from the plurality of nested objects of theassessment creation interface. The method can include: generating aweighting value for each of the selected objects, which weighting valueof an object identifies a relative contribution of the object to a levelin the hierarchical data structure; and generating an assessment fromdata packets associated with the selected objects according to theweighting value.

In some embodiments, the assessment creation interface includes: a firstplurality of objects, each object of the first plurality of objectscorresponding to one of a plurality of domains; a second plurality ofobjects, each object of which second plurality of objects correspondingto one of a plurality of clusters; and a third plurality of objects,each object of which third plurality of objects corresponding to one ofa plurality of standards. In some embodiments, each of the firstplurality of objects includes a first-object boundary and contains atleast one of the second plurality of objects nested within thefirst-object boundary. In some embodiments, each of the second pluralityof objects includes a second object boundary and contains at least oneof the third plurality of objects nested within the second objectboundary.

In some embodiments, at least one of the first plurality of objectscontains some of the second plurality of objects nested within thefirst-object boundary. In some embodiments, at least one of the secondobjects contains some of the plurality of third objects nested withinthe second object boundary.

In some embodiments, the method includes generating a confirmationinterface in response to receipt of selection of the first object andthe second object and receiving confirmation of selection of the firstobject and the second object. In some embodiments, each of the firstobject and the second object include one of the third plurality ofobjects. In some embodiments, generating a weighting value includes:retrieving a raw weighting value for each of the first object and thesecond object; and generating a normalized weighting value for each ofthe first object and the second object. In some embodiments, generatingthe normalized weighting value includes: identifying a common level inthe hierarchical structure, which common level includes an objectupstream coupled with each of the first object and the second object. Insome embodiments, the normalized weighting value is generated based onthe common level.

One aspect of the present disclosure relates to a system for automatedcontent selection and presentation. The system includes a memoryincluding a content item library including a hierarchical data structurehaving levels and a plurality of data packets, each of which datapackets is linked with at least a portion of the hierarchical datastructure. The system includes at least one server. The at least oneserver can: identify and deliver an item within a first content domainto a user device; evaluate a response to the delivered item; generate anestimated skill level, also referred to herein as a “scalar skilllevel”, with a unidimensional evaluation engine; select and present anext item based on the scalar estimated skill level; and upon completionof an assessment, generate a vector estimated skill level with amultidimensional evaluation engine. In some embodiments, the vectorestimated skill level is at least partially redundant with the scalarestimated skill level.

In some embodiments, the next item belongs to the first content domain.In some embodiments, the next item belongs to a second content domain.In some embodiments, the at least one server can: determine meeting ofat least one termination criteria of the first content domain; anddetermine completion of the first content domain when the at least onetermination criteria of the first content domain is met.

In some embodiments, the at least one server can deliver items withinthe content domain until the at least one termination criteria of thecontent domain are met. In some embodiments, the at least one server canselect a second content domain when the termination criteria of thefirst content domain is met. In some embodiments, the at least oneserver can estimate a user skill level in the second content domainbased on user response received in the first domain. In someembodiments, the next item in the second content domain is selectedbased on the estimated user skill level in the second content domain.

In some embodiments, the at least one server can select and deliver nextitems until at least one assessment termination criteria for theassessment is met. In some embodiments, the at least one server canlaunch a multidimensional evaluation engine when the at least oneassessment termination criteria is met. In some embodiments, the atleast one server can generate a vector skill level with themultidimensional evaluation engine. In some embodiments, evaluating theresponse to the delivered item includes: determining a correctness ofthe received response; and generating a response vector characterizingthe correctness of the received response. In some embodiments,evaluating the response to the delivered item further includesassociating the response vector with the content domain containing theitem associated with the response.

One aspect of the present disclosure relates to a method for automatedcontent selection and presentation. The method includes: identifying anddelivering an item within a first content domain to a user device;evaluating a response to the delivered item; generating a scalarestimated skill level with a unidimensional evaluation engine; selectingand presenting a next item based on the scalar estimated skill level;and upon completion of an assessment, generating a vector estimatedskill level with a multidimensional evaluation engine. In someembodiments, the vector estimated skill level is at least partiallyredundant with the scalar estimated skill level.

In some embodiments, the next item belongs to the first content domain.In some embodiments, the next item belongs to a second content domain.In some embodiments, the method includes: determining meeting of atleast one termination criteria of the first content domain; anddetermining completion of the first content domain when the at least onetermination criteria of the first content domain is met.

In some embodiments, the method includes: delivering items within thecontent domain until the at least one termination criteria of thecontent domain are met; and selecting a second content domain when thetermination criteria of the first content domain is met. In someembodiments, the method includes estimating a user skill level in thesecond content domain based on user response received in the firstdomain. In some embodiments, the next item in the second content domainis selected based on the estimated user skill level in the secondcontent domain.

In some embodiments, the method includes: selecting and delivering nextitems until at least one assessment termination criteria for theassessment is met; and launching a multidimensional evaluation enginewhen the at least one assessment termination criteria is met.

In some embodiments, the method includes generating a vector skill levelwith the multidimensional evaluation engine. In some embodiments,evaluating the response to the delivered item includes: determining acorrectness of the received response; generating a response vectorcharacterizing the correctness of the received response; and associatingthe response vector with a content domain containing the item associatedwith the response.

Further areas of applicability of the present disclosure will becomeapparent from the detailed description provided hereinafter. It shouldbe understood that the detailed description and specific examples, whileindicating various embodiments, are intended for purposes ofillustration only and are not intended to necessarily limit the scope ofthe disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing illustrating an example of a contentdistribution network.

FIG. 2 is a block diagram illustrating a computer server and computingenvironment within a content distribution network.

FIG. 3 is a block diagram illustrating an embodiment of one or more datastore servers within a content distribution network.

FIG. 4 is a block diagram illustrating an embodiment of one or morecontent management servers within a content distribution network.

FIG. 5 is a flowchart illustrating one embodiment of a process for datamanagement.

FIG. 6 is a flowchart illustrating one embodiment of a process forevaluating a response.

FIG. 7 is a block diagram illustrating the physical and logicalcomponents of a special-purpose computer device within a contentdistribution network.

FIG. 8 is a block diagram illustrating one embodiment of thecommunication network.

FIG. 9 is a block diagram illustrating one embodiment of user device andsupervisor device communication.

FIG. 10 is a schematic illustration of one embodiment of linked nodeswithin a network.

FIG. 11 is a schematic illustration of one embodiment of linked nodeswithin a network in which a probability associated with one of thelinked nodes is updated.

FIG. 12 is a schematic illustration of one embodiment of linked nodeswithin a network in which probabilities associated with two of thelinked nodes are updated, which two of the linked nodes can beassociated with different interim assessments.

FIG. 13 is a schematic depiction of one embodiment of a hierarchicaldata structure.

FIG. 14 is a graphical depiction of one embodiment of a creationinterface.

FIG. 15 is a graphical depiction of one embodiment of a confirmationinterface.

FIG. 16 is a schematic illustration of one embodiment of an assessmentgeneration and delivery system.

FIG. 17 is a flowchart illustrating one embodiment of a process forautomated assessment generation.

FIG. 18 is a flowchart illustrating one embodiment of process forgenerating a normalized weighting value.

FIG. 19 is a flowchart illustrating one embodiment of a process forautomated content selection and presentation.

In the appended figures, similar components and/or features may have thesame reference label. Further, various components of the same type maybe distinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If only the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label.

DETAILED DESCRIPTION

The ensuing description provides illustrative embodiment(s) only and isnot intended to limit the scope, applicability or configuration of thedisclosure. Rather, the ensuing description of the illustrativeembodiment(s) will provide those skilled in the art with an enablingdescription for implementing a preferred exemplary embodiment. It isunderstood that various changes can be made in the function andarrangement of elements without departing from the spirit and scope asset forth in the appended claims.

Assessment generation and delivery can create multiple problems that candecrease the effectiveness of any generated and/or delivered assessment.These problems can include, for example providing too many or too fewquestions, and/or providing questions that are improperly and/orinadequately tied to assessment outcomes. Use of computerized assessmenthas, at times, appeared to offer solutions to these problems, but thesesolutions have, as yet, been elusive. Particularly, the incorporation oftechnology into assessment creation has not improved the assessmentcreation process, but rather rendered the process more difficult andtime consuming. Many of these difficulties relate to large amounts ofcontent including questions that could be incorporated into anassessment. These large volumes of content create difficulties for boththe assessment author and the assessment creation software. Thesedifficulties can include limitations on access and use of content,mismatching of content with assessment objectives, and/or excessive useof processing bandwidth.

Some embodiments of the present disclosure relates to systems andmethods for addressing these difficulties and providing improvedtechnical solutions for assessment creation. Some such embodiments caninclude the generation of an assessment creation interface and/or aconfirmation interface. The assessment creation interface can displayinformation corresponding to a hierarchical structure organizing contentfor potential presentation as part of the assessment. This hierarchicalstructure can organize content into, for example, one or severalsub-standards, one or several standards, one or several clusters, one orseveral domains, and/or one or several subjects. In some embodiments,for example, one or several pieces of content, also referred to hereinas data packets, such as, for example, one or several questions can belinked together by any of: a substandard; a standard; a cluster; adomain; and a subject. In some embodiments, a sub-standard can compriseone or several data packets, a standard can comprise one or severalsub-standards and/or one or several data packets, a cluster can compriseone or several standards and/or one or several data packets, a domaincan comprise one or several clusters and/or one or several data packets,and subject can comprise one or several domains and/or one or severaldata packets.

The assessment creation information can provide a graphical display ofthe hierarchical structure using a nested objects to indicates thishierarchy. A user can select one or several object for inclusion in anassessment, and can confirm selection of these objects via theconfirmation interface. Weighting data can be retrieved from memory,which weighting data can be raw weighting data that identifies arelative importance of each selected object. The raw weighting data canbe normalized, which normalized weighting data can be used in selectionand presentation of data packets to a user.

Some embodiments relate to a multi-dimensional assessment engine. Themulti-dimensional assessment engine can provide an assessment to a user,which assessment can, in some embodiments, be created via the assessmentcreation interface. The multidimensional assessment engine can dividethe assessment into several domains. Data packets from a single domaincan be selected and provided to the user until a termination criteriafor that single domain is reached, at which point, data packets from anext domain can be selected and provided to the user. This selection andprovisioning of data packets to the user from a single domain can berepeated until a termination criteria for the assessment has beenreached and/or until termination criteria for all of the domains in theassessment have been fulfilled.

The multi-dimensional assessment engine can utilize two evaluationprocesses, a first evaluation process for selection of next content anda second evaluation process for generating a multi-dimensional score, orin other words, for generation of one or several scores relevant atmultiple levels in the hierarchical content structure. In someembodiments, the first evaluation process can utilize a one-dimensionalassessment engine, and in some embodiments, the second evaluationprocess can utilize a multi-dimensional assessment engine.

The one-dimensional assessment engine can generate an estimate of a userskill level with respect to a single domain in the assessment. Bylimiting the assessment engine to a single domain, the one-dimensionalassessment engine is able to improve processing times and more quicklygenerate an estimate of user skill level with respect to the user'scurrent domain, which improved processing time decreases lag timebetween receipt of a user-provided response and providing of nextcontent. Further, this improved processing time is particularlybeneficial in a network based environment as the restriction of anevaluation to one-dimension truncates the amount of data used ingenerating an estimated skill level and thus decrease the amount of datafor transferring across the network.

The multi-dimensional assessment engine can generate an estimated scorefor an assessment based on scores, as generated by one-dimensionalassessment engines, for each domain of the assessment. The estimatedscore generated by the multi-dimensional assessment engine can includecomponents relevant to multiple levels of the hierarchical structureassociated with the assessment. For example, the estimated score can berelevant to the assessment and can include components, each of which isrelevant to one of the domains of the assessment. This multi-dimensionalscore can utilize more computing resources, but the generation of thisscore is deferred until the assessment is completed and is generatedonce for an assessment. Thus, the combination of the scalar scores andthe multi-dimensional score provides the benefit of themulti-dimensional score while limiting the associated processing costs.

With reference now to FIG. 1, a block diagram is shown illustratingvarious components of a content distribution network (CDN) 100 whichimplements and supports certain embodiments and features describedherein. Content distribution network 100 may include one or more contentmanagement servers 102. As discussed below in more detail, contentmanagement servers 102 may be any desired type of server including, forexample, a rack server, a tower server, a miniature server, a bladeserver, a mini rack server, a mobile server, an ultra-dense server, asuper server, or the like, and may include various hardware components,for example, a motherboard, a processing units, memory systems, harddrives, network interfaces, power supplies, etc. Content managementserver 102 may include one or more server farms, clusters, or any otherappropriate arrangement and/or combination or computer servers. Contentmanagement server 102 may act according to stored instructions locatedin a memory subsystem of the server 102, and may run an operatingsystem, including any commercially available server operating systemand/or any other operating systems discussed herein.

The content distribution network 100 may include one or more data storeservers 104, also referred to herein as “databases”, such as databaseservers and/or file-based storage systems. The database servers 104 canaccess data that can be stored on a variety of hardware components.These hardware components can include, for example, components formingtier 0 storage, components forming tier 1 storage, components formingtier 2 storage, and/or any other tier of storage. In some embodiments,tier 0 storage refers to storage that is the fastest tier of storage inthe database server 104, and particularly, the tier 0 storage is thefastest storage that is not RAM or cache memory. In some embodiments,the tier 0 memory can be embodied in solid state memory such as, forexample, a solid-state drive (SSD) and/or flash memory.

In some embodiments, the tier 1 storage refers to storage that is one orseveral higher performing systems in the memory management system, andthat is relatively slower than tier 0 memory, and relatively faster thanother tiers of memory. The tier 1 memory can be one or several harddisks that can be, for example, high-performance hard disks. These harddisks can be one or both of physically or communicatingly connected suchas, for example, by one or several fiber channels. In some embodiments,the one or several disks can be arranged into a disk storage system, andspecifically can be arranged into an enterprise class disk storagesystem. The disk storage system can include any desired level ofredundancy to protect data stored therein, and in one embodiment, thedisk storage system can be made with grid architecture that createsparallelism for uniform allocation of system resources and balanced datadistribution.

In some embodiments, the tier 2 storage refers to storage that includesone or several relatively lower performing systems in the memorymanagement system, as compared to the tier 1 and tier 2 storages. Thus,tier 2 memory is relatively slower than tier 1 and tier 0 memories. Tier2 memory can include one or several SATA-drives or one or severalNL-SATA drives.

In some embodiments, the one or several hardware and/or softwarecomponents of the database server 104 can be arranged into one orseveral storage area networks (SAN), which one or several storage areanetworks can be one or several dedicated networks that provide access todata storage, and particularly that provides access to consolidated,block level data storage. A SAN typically has its own network of storagedevices that are generally not accessible through the local area network(LAN) by other devices. The SAN allows access to these devices in amanner such that these devices appear to be locally attached to the userdevice.

Databases 104 may comprise stored data relevant to the functions of thecontent distribution network 100. Illustrative examples of databases 104that may be maintained in certain embodiments of the contentdistribution network 100 are described below in reference to FIG. 3. Insome embodiments, multiple databases may reside on a single databaseserver 104, either using the same storage components of server 104 orusing different physical storage components to assure data security andintegrity between databases. In other embodiments, each database mayhave a separate dedicated database server 104.

Content distribution network 100 also may include one or more userdevices 106 and/or supervisor devices 110. User devices 106 andsupervisor devices 110 may display content received via the contentdistribution network 100, and may support various types of userinteractions with the content. User devices 106 and supervisor devices110 may include mobile devices such as smartphones, tablet computers,personal digital assistants, and wearable computing devices. Such mobiledevices may run a variety of mobile operating systems, and may beenabled for Internet, e-mail, short message service (SMS), Bluetooth®,mobile radio-frequency identification (M-RFID), and/or othercommunication protocols. Other user devices 106 and supervisor devices110 may be general purpose personal computers or special-purposecomputing devices including, by way of example, personal computers,laptop computers, workstation computers, projection devices, andinteractive room display systems. Additionally, user devices 106 andsupervisor devices 110 may be any other electronic devices, such asthin-client computers, Internet-enabled gaming system, business or homeappliances, and/or personal messaging devices, capable of communicatingover network(s) 120.

In different contexts of content distribution networks 100, user devices106 and supervisor devices 110 may correspond to different types ofspecialized devices, for example, student devices and teacher devices inan educational network, employee devices and presentation devices in acompany network, different gaming devices in a gaming network, etc. Insome embodiments, user devices 106 and supervisor devices 110 mayoperate in the same physical location 107, such as a classroom orconference room. In such cases, the devices may contain components thatsupport direct communications with other nearby devices, such as awireless transceivers and wireless communications interfaces, Ethernetsockets or other Local Area Network (LAN) interfaces, etc. In otherimplementations, the user devices 106 and supervisor devices 110 neednot be used at the same location 107, but may be used in remotegeographic locations in which each user device 106 and supervisor device110 may use security features and/or specialized hardware (e.g.,hardware-accelerated SSL and HTTPS, WS-Security, firewalls, etc.) tocommunicate with the content management server 102 and/or other remotelylocated user devices 106. Additionally, different user devices 106 andsupervisor devices 110 may be assigned different designated roles, suchas presenter devices, teacher devices, administrator devices, or thelike, and in such cases the different devices may be provided withadditional hardware and/or software components to provide content andsupport user capabilities not available to the other devices.

The content distribution network 100 also may include a privacy server108 that maintains private user information at the privacy server 108while using applications or services hosted on other servers. Forexample, the privacy server 108 may be used to maintain private data ofa user within one jurisdiction even though the user is accessing anapplication hosted on a server (e.g., the content management server 102)located outside the jurisdiction. In such cases, the privacy server 108may intercept communications between a user device 106 or supervisordevice 110 and other devices that include private user information. Theprivacy server 108 may create a token or identifier that does notdisclose the private information and may use the token or identifierwhen communicating with the other servers and systems, instead of usingthe user's private information.

As illustrated in FIG. 1, the content management server 102 may be incommunication with one or more additional servers, such as a contentserver 112, a user data server 112, and/or an administrator server 116.Each of these servers may include some or all of the same physical andlogical components as the content management server(s) 102, and in somecases, the hardware and software components of these servers 112-116 maybe incorporated into the content management server(s) 102, rather thanbeing implemented as separate computer servers.

Content server 112 may include hardware and software components togenerate, store, and maintain the content resources for distribution touser devices 106 and other devices in the network 100. For example, incontent distribution networks 100 used for professional training andeducational purposes, content server 112 may include databases oftraining materials, presentations, plans, syllabi, reviews, evaluations,interactive programs and simulations, course models, course outlines,and various training interfaces that correspond to different materialsand/or different types of user devices 106. In content distributionnetworks 100 used for media distribution, interactive gaming, and thelike, a content server 112 may include media content files such asmusic, movies, television programming, games, and advertisements.

User data server 114 may include hardware and software components thatstore and process data for multiple users relating to each user'sactivities and usage of the content distribution network 100. Forexample, the content management server 102 may record and track eachuser's system usage, including their user device 106, content resourcesaccessed, and interactions with other user devices 106. This data may bestored and processed by the user data server 114, to support usertracking and analysis features. For instance, in the professionaltraining and educational contexts, the user data server 114 may storeand analyze each user's training materials viewed, presentationsattended, courses completed, interactions, evaluation results, and thelike. The user data server 114 may also include a repository foruser-generated material, such as evaluations and tests completed byusers, and documents and assignments prepared by users. In the contextof media distribution and interactive gaming, the user data server 114may store and process resource access data for multiple users (e.g.,content titles accessed, access times, data usage amounts, gaminghistories, user devices and device types, etc.).

Administrator server 116 may include hardware and software components toinitiate various administrative functions at the content managementserver 102 and other components within the content distribution network100. For example, the administrator server 116 may monitor device statusand performance for the various servers, databases, and/or user devices106 in the content distribution network 100. When necessary, theadministrator server 116 may add or remove devices from the network 100,and perform device maintenance such as providing software updates to thedevices in the network 100. Various administrative tools on theadministrator server 116 may allow authorized users to set user accesspermissions to various content resources, monitor resource usage byusers and devices 106, and perform analyses and generate reports onspecific network users and/or devices (e.g., resource usage trackingreports, training evaluations, etc.).

The content distribution network 100 may include one or more surveyservers 119. The survey server 119 may include hardware and softwarecomponents to generate, store, and maintain the survey resources fordistribution to user devices 106 and other devices in the network 100.In some embodiments, the survey server 119 can send survey informationto one or several of the user devices 106 and/or receive surveyinformation from one or several of the user devices 106.

In some embodiments, the survey server 119 can be configured to generateand/or aggregate one or several surveys based on questions received froma user device 106 and/or a supervisor device 110. In some embodiments,the survey server 119 can be configured to generate and/or aggregate oneor several surveys based on questions stored in a database in thedatabase server 104.

In some embodiments, the survey server 119 can be configured to receive,sort, and/or analyze some or all of the survey information received fromthe one or several user devices 106. In some embodiments, the surveyserver 119 can receive the survey information, classify the surveyinformation, and direct the storage of the survey information within oneor several of the databases of the database server 104 according to oneor several attributes of the survey information. In some embodiments,these one or several attributes can, for example, relate to whether thesurvey information is of the type used for providing real-time feedback,or of the type that is not used for providing real-time feedback.

By way of example, in some embodiments, survey information can bereceived during, for example, a lecture, a class, or the like, and canbe used to affect a portion of that lecture, class, or the like. In suchan embodiment, the survey information can be analyzed to determine theeffectiveness of the lecture, the class, or the like and feedback can beprovided during the lecture, class, or the like based on the analysis ofthe survey data. As used herein, feedback is provided in real-time iffeedback is provided before the completion of the lecture, class, or thelike from which survey data was collected upon which the feedback isbased.

In such an embodiment in which real-time feedback is desired, the speedwith which the survey data is accessible and analyzable can determinewhether timely, real-time feedback can be provided. Thus, in someembodiments, such survey information for which timely, real-timefeedback may be desired can be directed for storage in a databaselocated in a tier 0 or tier 1 memory, and survey information for whichreal-time feedback is not desired may be directed for storage in adatabase located in a lower tier memory.

The content distribution network 100 may include one or morecommunication networks 120. Although only a single network 120 isidentified in FIG. 1, the content distribution network 100 may includeany number of different communication networks between any of thecomputer servers and devices shown in FIG. 1 and/or other devicesdescribed herein. Communication networks 120 may enable communicationbetween the various computing devices, servers, and other components ofthe content distribution network 100. As discussed below, variousimplementations of content distribution networks 100 may employdifferent types of networks 120, for example, computer networks,telecommunications networks, wireless networks, and/or any combinationof these and/or other networks.

In some embodiments, some of the components of the content distributionnetwork 100 can be identified as being part of the back-end components122. The back-end components 122 can include, for example, the contentmanagement server 102, the database server 1204, the privacy server 108,the content server 112, the user data server 114, the administratorserver 116, and/or the communication network 120.

The content distribution network 100 may include one or severalnavigation systems or features including, for example, the GlobalPositioning System (“GPS”), GALILEO, or the like, or location systems orfeatures including, for example, one or several transceivers that candetermine location of the one or several components of the contentdistribution network 100 via, for example, triangulation. All of theseare depicted as navigation system 124.

In some embodiments, navigation system 124 can include or severalfeatures that can communicate with one or several components of thecontent distribution network 100 including, for example, with one orseveral of the user devices 106 and/or with one or several of thesupervisor devices 110. In some embodiments, this communication caninclude the transmission of a signal from the navigation system 124which signal is received by one or several components of the contentdistribution network 100 and can be used to determine the location ofthe one or several components of the content distribution network 100.

With reference to FIG. 2, an illustrative distributed computingenvironment 200 is shown including a computer server 202, four clientcomputing devices 206, and other components that may implement certainembodiments and features described herein. In some embodiments, theserver 202 may correspond to the content management server 102 discussedabove in FIG. 1, and the client computing devices 206 may correspond tothe user devices 106. However, the computing environment 200 illustratedin FIG. 2 may correspond to any other combination of devices and serversconfigured to implement a client-server model or other distributedcomputing architecture.

Client devices 206 may be configured to receive and execute clientapplications over one or more networks 220. Such client applications maybe web browser based applications and/or standalone softwareapplications, such as mobile device applications. Server 202 may becommunicatively coupled with the client devices 206 via one or morecommunication networks 220. Client devices 206 may receive clientapplications from server 202 or from other application providers (e.g.,public or private application stores). Server 202 may be configured torun one or more server software applications or services, for example,web-based or cloud-based services, to support content distribution andinteraction with client devices 206. Users operating client devices 206may in turn utilize one or more client applications (e.g., virtualclient applications) to interact with server 202 to utilize the servicesprovided by these components.

Various different subsystems and/or components 204 may be implemented onserver 202. Users operating the client devices 206 may initiate one ormore client applications to use services provided by these subsystemsand components. The subsystems and components within the server 202 andclient devices 206 may be implemented in hardware, firmware, software,or combinations thereof. Various different system configurations arepossible in different distributed computing systems 200 and contentdistribution networks 100. The embodiment shown in FIG. 2 is thus oneexample of a distributed computing system and is not intended to belimiting.

Although exemplary computing environment 200 is shown with four clientcomputing devices 206, any number of client computing devices may besupported. Other devices, such as specialized sensor devices, etc., mayinteract with client devices 206 and/or server 202.

As shown in FIG. 2, various security and integration components 208 maybe used to send and manage communications between the server 202 anduser devices 206 over one or more communication networks 220. Thesecurity and integration components 208 may include separate servers,such as web servers and/or authentication servers, and/or specializednetworking components, such as firewalls, routers, gateways, loadbalancers, and the like. In some cases, the security and integrationcomponents 208 may correspond to a set of dedicated hardware and/orsoftware operating at the same physical location and under the controlof same entities as server 202. For example, components 208 may includeone or more dedicated web servers and network hardware in a datacenteror a cloud infrastructure. In other examples, the security andintegration components 208 may correspond to separate hardware andsoftware components which may be operated at a separate physicallocation and/or by a separate entity.

Security and integration components 208 may implement various securityfeatures for data transmission and storage, such as authenticating usersand restricting access to unknown or unauthorized users. In variousimplementations, security and integration components 208 may provide,for example, a file-based integration scheme or a service-basedintegration scheme for transmitting data between the various devices inthe content distribution network 100. Security and integrationcomponents 208 also may use secure data transmission protocols and/orencryption for data transfers, for example, File Transfer Protocol(FTP), Secure File Transfer Protocol (SFTP), and/or Pretty Good Privacy(PGP) encryption.

In some embodiments, one or more web services may be implemented withinthe security and integration components 208 and/or elsewhere within thecontent distribution network 100. Such web services, includingcross-domain and/or cross-platform web services, may be developed forenterprise use in accordance with various web service standards, such asRESTful web services (i.e., services based on the Representation StateTransfer (REST) architectural style and constraints), and/or webservices designed in accordance with the Web Service Interoperability(WS-I) guidelines. For example, some web services may use the SecureSockets Layer (SSL) or Transport Layer Security (TLS) protocol toprovide secure connections between the server 202 and user devices 206.SSL or TLS may use HTTP or HTTPS to provide authentication andconfidentiality. In other examples, web services may be implementedusing REST over HTTPS with the OAuth open standard for authentication,or using the WS-Security standard, which provides for secure SOAPmessages using XML encryption. In other examples, the security andintegration components 208 may include specialized hardware forproviding secure web services. For example, security and integrationcomponents 208 may include secure network appliances having built-infeatures such as hardware-accelerated SSL and HTTPS, WS-Security, andfirewalls. Such specialized hardware may be installed and configured infront of any web servers, so that any external devices may communicatedirectly with the specialized hardware.

Communication network(s) 220 may be any type of network familiar tothose skilled in the art that can support data communications using anyof a variety of commercially-available protocols, including withoutlimitation, TCP/IP (transmission control protocol/Internet protocol),SNA (systems network architecture), IPX (Internet packet exchange),Secure Sockets Layer (SSL) or Transport Layer Security (TLS) protocols,Hyper Text Transfer Protocol (HTTP) and Secure Hyper Text TransferProtocol (HTTPS), Bluetooth®, Near Field Communication (NFC), and thelike. Merely by way of example, network(s) 220 may be local areanetworks (LAN), such as one based on Ethernet, Token-Ring and/or thelike. Network(s) 220 also may be wide-area networks, such as theInternet. Networks 220 may include telecommunication networks such as apublic switched telephone networks (PSTNs), or virtual networks such asan intranet or an extranet. Infrared and wireless networks (e.g., usingthe Institute of Electrical and Electronics (IEEE) 802.11 protocol suiteor other wireless protocols) also may be included in networks 220.

Computing environment 200 also may include one or more databases 210and/or back-end servers 212. In certain examples, the databases 210 maycorrespond to database server(s) 104 discussed above in FIG. 1, andback-end servers 212 may correspond to the various back-end servers112-116. Databases 210 and servers 212 may reside in the same datacenteror may operate at a remote location from server 202. In some cases, oneor more databases 210 may reside on a non-transitory storage mediumwithin the server 202. Other databases 210 and back-end servers 212 maybe remote from server 202 and configured to communicate with server 202via one or more networks 220. In certain embodiments, databases 210 andback-end servers 212 may reside in a storage-area network (SAN), or mayuse storage-as-a-service (STaaS) architectural model. In someembodiments, the computing environment can be replicated for each of thenetworks 107, 122, 104 discussed with respect to FIG. 1 above.

With reference to FIG. 3, an illustrative set of databases and/ordatabase servers is shown, corresponding to the databases servers 104 ofthe content distribution network 100 discussed above in FIG. 1. One ormore individual databases 301-312 may reside in storage on a singlecomputer server 104 (or a single server farm or cluster) under thecontrol of a single entity, or may reside on separate servers operatedby different entities and/or at remote locations. In some embodiments,databases 301-312 may be accessed by the content management server 102and/or other devices and servers within the network 100 (e.g., userdevices 106, supervisor devices 110, administrator servers 116, etc.).Access to one or more of the databases 301-312 may be limited or deniedbased on the processes, user credentials, and/or devices attempting tointeract with the database.

The paragraphs below describe examples of specific databases that may beimplemented within some embodiments of a content distribution network100. It should be understood that the below descriptions of databases301-312, including their functionality and types of data stored therein,are illustrative and non-limiting. Database server architecture, design,and the execution of specific databases 301-312 may depend on thecontext, size, and functional requirements of a content distributionnetwork 100. For example, in content distribution systems 100 used forprofessional training and educational purposes, separate databases orfile-based storage systems may be implemented in database server(s) 104to store trainee and/or student data, trainer and/or professor data,training module data and content descriptions, training results,evaluation data, and the like. In contrast, in content distributionsystems 100 used for media distribution from content providers tosubscribers, separate databases may be implemented in database server(s)104 to store listing of available content titles and descriptions,content title usage statistics, subscriber profiles, account data,payment data, network usage statistics, etc.

A user profile data store 301, also referred to herein as a user profiledatabase 301 may include information relating to the end users withinthe content distribution network 100. This information may include usercharacteristics such as the user names, access credentials (e.g., loginsand passwords), user preferences, and information relating to anyprevious user interactions within the content distribution network 100(e.g., requested content, posted content, content modules completed,training scores or evaluations, other associated users, etc.). In someembodiments, this information can relate to one or several individualend users such as, for example, one or several individuals, contentauthors, teachers, administrators, or the like, and in some embodiments,this information can relate to one or several institutional end userssuch as, for example, one or several schools, groups of schools such asone or several school districts, one or several colleges, one or severaluniversities, one or several training providers, or the like.

In some embodiments, this information can identify one or several usermemberships in one or several groups such as, for example, anindividual's membership in a university, school, program, grade, course,class, or the like. In some embodiments, the user profile database 301can include information relating to a user's status, location, or thelike. This information can identify, for example, a device a user isusing, the location of that device, or the like. In some embodiments,this information can be generated based on any location detectiontechnology including, for example, a navigation system 122, or the like.

Information relating to the user's status can identify, for example,logged-in status information that can indicate whether the user ispresently logged-in to the content distribution network 100 and/orwhether the log-in-is active. In some embodiments, the informationrelating to the user's status can identify whether the user is currentlyaccessing content and/or participating in an activity from the contentdistribution network 100.

In some embodiments, information relating to the user's status canidentify, for example, one or several attributes of the user'sinteraction with the content distribution network 100, and/or contentdistributed by the content distribution network 100. This can includedata identifying the user's interactions with the content distributionnetwork 100, the content consumed by the user through the contentdistribution network 100, or the like. In some embodiments, this caninclude data identifying the type of information accessed through thecontent distribution network 100 and/or the type of activity performedby the user via the content distribution network 100, the lapsed timesince the last time the user accessed content and/or participated in anactivity from the content distribution network 100, or the like. In someembodiments, this information can relate to a content program comprisingan aggregate of data, content, and/or activities, and can identify, forexample, progress through the content program, or through the aggregateof data, content, and/or activities forming the content program. In someembodiments, this information can track, for example, the amount of timesince participation in and/or completion of one or several types ofactivities, the amount of time since communication with one or severalsupervisors and/or supervisor devices 110, or the like.

In some embodiments in which the one or several end users areindividuals, and specifically are students, the user profile database301 can further include information relating to a student's academicand/or educational history. This information can identify one or severalcourses of study that the individual has initiated, completed, and/orpartially completed, as well as grades received in those courses ofstudy. In some embodiments, the individual's academic and/or educationalhistory can further include information identifying individualperformance on one or several evaluations, quizzes, and/or assignments.In some embodiments, this information can be stored in a tier of memorythat is not the fastest memory in the content distribution network 100.

The user profile database 301 can include information relating to one orseveral student learning preferences. In some embodiments, for example,the user, also referred to herein as the student, the student-user, oras the recipient-user, may have one or several preferred learningstyles, one or several most effective learning styles, and/or the like.In some embodiments, the individual's learning style can be any learningstyle describing how the individual best learns or how the individualprefers to learn. In one embodiment, these learning styles can include,for example, identification of the individual as an auditory learner, asa visual learner, and/or as a tactile learner. In some embodiments, thedata identifying one or several individual learning styles can includedata identifying a learning style based on the individual's educationalhistory such as, for example, identifying an individual as an auditorylearner when the individual has received significantly higher gradesand/or scores on assignments and/or in courses favorable to auditorylearners. In some embodiments, this information can be stored in a tierof memory that is not the fastest memory in the content distributionnetwork 100.

In some embodiments, the user profile database 301 can includeinformation relating to one or several recipient-user behaviorsincluding, for example: attendance in one or several courses; attendanceand/or participation in one or several study groups; extramural, studentgroup, and/or club involve and/or participation, or the like. In someembodiments, this information relating to one or several recipient-userbehaviors can include information relating to the recipient-usersschedule.

In some embodiments, the user profile database 301 can includeinformation identifying one or several skill levels of some or all ofthe users identified in the user profile database 301. In someembodiments, these skill levels can identify, for example, mastery ofall or portions of a standard. This identification of mastery of all orportions of the standard can, in some embodiments, identify one orseveral portions of the standard where mastery has not been achieved,and can, in some embodiments, identify one or several action plansand/or remediation plans for attaining mastery of one, some, or all ofthose one or several portions of the standard where mastery has not beenachieved.

In some embodiments, the user profile database can include a database ofresponses received from the user. In some embodiments, this database caninclude information identifying one or several received responses,information identifying the evaluation of those one or several receivedresponses, and/or information identifying the items for which theresponses were received. In some embodiments, this database of responsescan include a response vector for each received response. The responsevector can, in some embodiments, be generated by the server 102, and cancharacterize one or several attributes of the received response. In someembodiments, the response vector can characterize one or severalattributes of the received response including, for example, the resultof the evaluation of the received responses. In some embodiments, thecharacterization of the received responses, each with a response vectorcan improve operation of the CDN 100, and specifically can improve thespeed with which responses can be ingested by a statistical learningmodel to generate, for example, a predicted and/or estimated user skilllevel. In some embodiments, the response vector can be generated by theextraction of one or several features from the received response and theconversion of these features into a format suitable for entry in avector. This database can be updated as responses are received from auser.

The user profile database 301 can further include information relatingto one or several teachers, presenters, and/or instructors who areresponsible for organizing, presenting, and/or managing the presentationof information to the student. In some embodiments, user profiledatabase 301 can include information identifying courses and/or subjectsthat have been taught by the presenter, data identifying courses and/orsubjects currently taught by the presenter, and/or data identifyingcourses and/or subjects that will be taught by the presenter. In someembodiments, this can include information relating to one or severalteaching styles of one or several presenters. In some embodiments, theuser profile database 301 can further include information indicatingpast evaluations and/or evaluation reports received by the presenter. Insome embodiments, the user profile database 301 can further includeinformation relating to improvement suggestions received by thepresenter, training received by the presenter, continuing educationreceived by the presenter, and/or the like. In some embodiments, thisinformation can be stored in a tier of memory that is not the fastestmemory in the content distribution network 100.

An accounts datastore 302, also referred to herein as an accountsdatabase 302, may generate and store account data for different users invarious roles within the content distribution network 100. For example,accounts may be created in an accounts database 302 for individual endusers, supervisors, administrator users, and entities such as companiesor educational institutions. Account data may include account types,current account status, account characteristics, and any parameters,limits, restrictions associated with the accounts.

A content library datastore 303, also referred to herein as a contentlibrary database 303, may include information describing the individualcontent items (or content resources or data packets) available via thecontent distribution network 100. In some embodiments, the librarydatabase 303 may include metadata, properties, and other characteristicsassociated with the content resources stored in the content server 112.In some embodiments, this data can include the one or several items thatcan include one or several documents and/or one or several applicationsor programs. In some embodiments, the one or several items can include,for example, one or several webpages, presentations, papers, videos,charts, graphs, books, written work, figures, images, graphics,recordings, or any other document, or any desired software orapplication or component thereof including, for example, a graphicaluser interface (GUI), all or portions of a Learning Management System(LMS), all or portions of a Content Management System (CMS), all orportions of a Student Information Systems (SIS), or the like.

In some embodiments, the data in the content library database 303 mayidentify one or more aspects or content attributes of the associatedcontent resources, for example, subject matter, access level, or skilllevel of the content resources, license attributes of the contentresources (e.g., any limitations and/or restrictions on the licensableuse and/or distribution of the content resource), price attributes ofthe content resources (e.g., a price and/or price structure fordetermining a payment amount for use or distribution of the contentresource), rating attributes for the content resources (e.g., dataindicating the evaluation or effectiveness of the content resource), andthe like. In some embodiments, the library database 303 may beconfigured to allow updating of content metadata or properties, and toallow the addition and/or removal of information relating to the contentresources. In some embodiments, the content library database 303 can beorganized such that content is associated with one or several coursesand/or programs in which the content is used and/or provided. In someembodiments, the content library database 303 can further include one orseveral teaching materials used in the course, a syllabus, one orseveral practice problems, one or several evaluations, one or severalquizzes, one or several assignments, or the like. All or portions of thecontent library database can be stored in a tier of memory that is notthe fastest memory in the content distribution network 100. For example,content relationships may be implemented as graph structures, which maybe stored in the library data store 303 or in an additional store foruse by selection algorithms along with the other metadata.

In some embodiments, the content library database 303 can compriseinformation to facilitate in authoring new content. This information cancomprise, for example, one or several specifications identifyingattributes and/or requirements of desired newly authored content. Insome embodiments, for example, a content specification can identify oneor several of a subject matter; length, difficulty level, or the likefor desired newly authored content.

In some embodiments, the content library database 303 can furtherinclude information for use in evaluating newly authored content. Insome embodiments, this evaluation can comprise a determination ofwhether and/or the degree to which the newly authored contentcorresponds to the content specification, or some or all of therequirements of the content specification. In some embodiments, thisinformation for use in evaluation newly authored content can identify ordefine one or several difficulty levels and/or can identify or defineone or several acceptable difficulty levels. In some embodiments, forexample, this information for use in evaluation newly authored contentcan define a plurality of difficulty levels and can delineate betweenthese difficulty levels, and in some embodiments, this information foruse in evaluation newly authored content can identify which of thedefined difficulty levels are acceptable. In other embodiments, thisinformation for use in evaluation newly authored content can merelyinclude one or several definitions of acceptable difficulty levels,which acceptable difficulty level can be based on one or severalpre-existing difficult measures such as, for example, an Item ResponseTheory (IRT) value such as, for example, an IRT b value, a p valueindicative of the proportion of correct responses in a set of responses,a grade level, or the like.

In some embodiments, this information for use in evaluation newlyauthored content can further define one or several differentiationand/or discrimination levels and/or define one or several acceptabledifferentiation and/or discrimination levels or ranges. As used herein,“differentiation” and “discrimination” refer to the degree to which anitem such as a question identifies low ability versus high abilityusers. In some embodiments, this information for use in evaluation newlyauthored content can identify one or several acceptable levels and/orranges of discrimination which levels and/or ranges can be based on oneor several currently existing discrimination measures such as, forexample, a Point-Biserial Correlation.

The content library database 303 can, in some embodiments, includeinformation identifying one or several standards. This informationidentifying one or several standards can be organized in a standardsdatabase located in the content library database 303. In someembodiments, a standard can define a plurality of subject matters and/orskills for which mastery is determined and/or measured. In someembodiments, the standard can include information identifying whatmastery is and/or how mastery is achieved of these subject mattersand/or skills. In some embodiments, for example, this informationidentifying what mastery is and/or how mastery is attained can specifyone or several thresholds delineating between mastery and non-mastery ofsome or all of these subject matters and/or skills. In some embodiments,this information can define a minimum mastery probability to be achievedbefore a user is identified as achieving mastery of some or all of thesesubject matters and/or skills.

In some embodiments, the standard can further include informationidentifying and/or defining attainment and/or meeting of the standard.In some embodiments, this can identify one or several thresholdsdelineating between circumstances of attainment and/or meeting of thestandard and circumstances of non-attainment and/or non-meeting of thestandard. In some embodiments, for example, this information identifyingand/or defining attainment and/or meeting of the standard can specify anumber and/or percent of the subject matters and/or skills to bemastered in order for the standard to be attained and/or met.

The content library database 303 can include information identifying oneor several items. These one or several items can comprise one or severalquestions for providing to one or several users in one or several tests.In some embodiments, responses received to these one or severalquestions can be evaluated and can be used as evidence of mastery of allor a portion of one or several standards. These questions can comprise,for example, one or several true/false questions, one or severalmultiple-choice questions, one or several essay a questions, one orseveral fill-in-the-blank questions, one or several short answerquestions, or the like. These one or several items can be stored in anitem bank, also referred to herein as an item database, a questiondatabase, or a content database. The item bank can be a sub databasewithin the content library database 303.

The content library database 303 can include a hierarchical datastructure and/or can partition and store the one or several itemsaccording to the hierarchical data structure. The hierarchical datastructure can include a plurality of levels, each level corresponding toa level of abstraction. In some embodiments, the hierarchical datastructure can include one or several levels having high levels ofabstraction and one or several levels having low levels of abstraction.In some embodiments, each of the levels of the hierarchical datastructure can be divided into one or several categories. In someembodiments, a level of the hierarchical data structure can include oneor several categories from one or several lower levels of abstraction.Further details of one exemplary hierarchical data structure will bediscussed in detail below.

The content library database 303 can include information relating to oneor several instructional units. In some embodiments, and instructionalunit can comprise content for providing to a user to teach a user skilland/or to develop user knowledge. Each instructional unit can includecontent for presenting to a recipient-user. This content can include,for example, teaching material, practice material, homework, and/orassessment material. This assessment material can include, for example,one or several interim assessments. This content can be in the form ofdigital written material such as written material containing one orseveral text strings or character strings, video content, audio content,one or several images, one or several simulations, or the like the oneor several instructional units can be stored in an instructional unitdatabase they can be, for example, a sub database within the contentlibrary database 303.

The content library database 303 can include a relational table,referred to herein as the Q-matrix. In some embodiments, the Q-matrixcan include information summarizing a relationship between curricularand instructional units, and how these, both the curricular andinstructional units relate to a standard. In some embodiments, theQ-matrix can include information linking the standards and/or portionsthereof to instructional units and/or two items in the item bank. Insome embodiments, each standard can have Q-matrix, and in otherembodiments, a single Q-matrix can include multiple standards. In oneembodiment, for example, the Q-matrix can link one or several interimassessments with content such as, for example, the assessment content orquestions, and the Q-matrix can link the one or several interimassessments with one or several standards and/or portions of standards.As used herein, an interim assessment is an independent assessment thatcovers a disaggregated portion and/or a subset of the content of thecumulative assessment. In some embodiments, for example, a cumulativetest may be given at the end of a course or time period, such as, forexample, at the end of a grade year, to determine compliance with astandard, whereas an interim test can be given before the end of thecourse or time period to determine compliance with a portion of thestandard. In some embodiments, the link between the interim assessment,and the one or several standards or portions of the standards can beinteract, such as, for example, when the one or several interimassessments are linked to assessment content, which assessment contentis likewise linked to one or several standards or portions of standards.In some embodiments, the Q-matrix can comprise a plurality of rows and aplurality of columns. In some embodiments, a standard and/or componentsof a standard can be identified in the rows of the Q-matrix. In someembodiments, a standard and/or components of a standard can beidentified in the rows of the Q-matrix such that each portion of thestandard for which mastery is determinable has a unique row. In someembodiments, the instructional units can be identified in the columns ofthe Q-matrix. Specifically, in some embodiments, each instructional unitrelevant to the one or several standards contained in the Q-matrix canhave a unique column. In some embodiments, items can be identified in a3^(rd) dimension of the Q-matrix linked in the Q-matrix to both theirassociated row and column.

The content library database 303 can include an evidence database. Inevidence database can include received responses. These responses can beorganized in the evidence database according to the item for which theresponse was received. In some embodiments, the evidence database canfurther include information identifying the evaluation of the receivedresponse, the student source of the received response, or the like.

A pricing database 304 may include pricing information and/or pricingstructures for determining payment amounts for providing access to thecontent distribution network 100 and/or the individual content resourceswithin the network 100. In some cases, pricing may be determined basedon a user's access to the content distribution network 100, for example,a time-based subscription fee, or pricing based on network usage and. Inother cases, pricing may be tied to specific content resources. Certaincontent resources may have associated pricing information, whereas otherpricing determinations may be based on the resources accessed, theprofiles and/or accounts of the users, and the desired level of access(e.g., duration of access, network speed, etc.). Additionally, thepricing database 304 may include information relating to compilationpricing for groups of content resources, such as group prices and/orprice structures for groupings of resources.

A license database 305 may include information relating to licensesand/or licensing of the content resources within the contentdistribution network 100. For example, the license database 305 mayidentify licenses and licensing terms for individual content resourcesand/or compilations of content resources in the content server 112, therights holders for the content resources, and/or common or large-scaleright holder information such as contact information for rights holdersof content not included in the content server 112.

A content access database 306 may include access rights and securityinformation for the content distribution network 100 and specificcontent resources. For example, the content access database 306 mayinclude login information (e.g., user identifiers, logins, passwords,etc.) that can be verified during user login attempts to the network100. The content access database 306 also may be used to store assignedroles and/or levels of access to users. For example, a user's accesslevel may correspond to the sets of content resources and/or the clientor server applications that the user is permitted to access. Certainusers may be permitted or denied access to certain applications andresources based on their subscription level, training program,course/grade level, etc. Certain users may have supervisory access overone or more end users, allowing the supervisor to access all or portionsof the end user's content, activities, evaluations, etc. Additionally,certain users may have administrative access over some users and/or someapplications in the content management network 100, allowing such usersto add and remove user accounts, modify user access permissions, performmaintenance updates on software and servers, etc.

A source datastore 307, also referred to herein as a source database307, may include information relating to the source of the contentresources available via the content distribution network. For example, asource database 307 may identify the authors and originating devices ofcontent resources, previous pieces of data and/or groups of dataoriginating from the same authors or originating devices, and the like.

An evaluation datastore 308, also referred to herein as an evaluationdatabase 308, may include information used to direct the evaluation ofusers and content resources in the content management network 100. Insome embodiments, the evaluation database 308 may contain, for example,the analysis criteria and the analysis guidelines for evaluating users(e.g., trainees/students, gaming users, media content consumers, etc.)and/or for evaluating the content resources in the network 100. Theevaluation database 308 also may include information relating toevaluation processing tasks, for example, the identification of usersand user devices 106 that have received certain content resources oraccessed certain applications, the status of evaluations or evaluationhistories for content resources, users, or applications, and the like.Evaluation criteria may be stored in the evaluation database 308including data and/or instructions in the form of one or severalelectronic rubrics or scoring guides for use in the evaluation of thecontent, users, or applications. The evaluation database 308 also mayinclude past evaluations and/or evaluation analyses for users, content,and applications, including relative rankings, characterizations,explanations, and the like.

A model data store 309, also referred to herein as a model database 309can store information relating to one or several predictive models. Insome embodiments, these one or several predictive models can be used to:generate a prediction of the risk of a recipient-user not achieving oneor several predetermined outcomes; generate a prediction of acategorization of the recipient-user, which categorization can indicatean expected effect of one or several interventions on therecipient-user; and/or generate a prediction of a priority for anyidentified intervention.

In some embodiments, the risk model can comprise one or severalpredictive models based on, for example, one or several computerlearning techniques. In some embodiments, the risk model can be used togenerate a risk value for an individual, which risk value characterizesthe risk of the recipient-user not achieving the predetermined outcomesuch as, for example, failing to complete a course or course of study,failing to graduate, failing to achieve a desired score or grade, or thelike. In some embodiments, the risk model can comprise, for example, adecision tree learning model. In some embodiments, the risk model cangenerate the risk value through the inputting of one or severalparameters, which parameters can be one or several values, into the riskmodel. These parameters can be generated based on one or severalfeatures or attributes of the recipient-user. The risk model, havingreceived the input parameters, can then generate the risk value.

In some embodiments, the categorization model can determine a categoryof the recipient-user. In some embodiments, the categorization model canbe used to generate one or several categorization values or identifiersthat identify a category of the recipient-user. In some embodiments,this category can correspond to a likelihood of an interventionincreasing or decreasing the risk value. In some embodiments, thecategories can comprise a first category in which an interventiondecreases the risk value, a second category in which an interventionincreases the risk value, and a third category in which an interventionwill not affect the risk value. In some embodiments, this third categorycan be further divided into a first group in which the recipient-userswill likely fail to achieve the desired outcome regardless ofintervention, and a second group in which the recipient-users willlikely achieve the desired outcome regardless of intervention. In someembodiments, the categorization model can determine the category of therecipient-user through the input of one or several parameters relevantto the recipient-user into the categorization model. In someembodiments, these parameters can be generated from one or severalfeatures or attributes of the recipient-user that can be, for example,extracted from data relating to the recipient-user.

In some embodiments, the priority model can determine a priority value,which can be a prediction of the importance of any determinedintervention. In some embodiments, this priority model can be determinedbased on information relating to the recipient-user for which thepriority value is determined. In some embodiments, this priority valuecan be impacted by, for example, the value of the course associated withthe risk value. In some embodiments, for example, the priority value mayindicate a lower priority for a risk in a non-essential course. In suchan embodiment, priority can be determined based on the credits of acourse, based on the relevance of a course to, for example, a degree ormajor, based on the role of the course as a pre-requisite to subsequentcourses, or the like.

In some embodiments, the model database 309 can comprise a networkdatabase. The network database can comprise information identifying astatistical model such as, for example, a probabilistic graphical modelor a probabilistic structural model. In some embodiments, theprobabilistic graphical model can comprise a probabilistic directedacyclic graphical model such as, for example, a Bayesian network, alatent-class model, an item-response model, a cognitive diagnosticmodel. In some embodiments, the Bayesian network can represent theprobabilistic relationships between items in the item bank andattainment of a standard and/or mastery of a portion of the standard. Insome embodiments, the items in the item bank and there linkedstandard(s) or portion(s) of the standard are linked by edgesrepresentative of conditional dependencies. In some embodiments, eachnode, which can include, for example, an item, a standard, and/or aportion of a standard, in the Bayesian network is associated with aprobability function that takes, as input, a particular set of valuesfor that nodes parent variables and gives as an output the probabilityof the variable represented by the node.

Embodiments of nodes linked within the Bayesian network 650 are shown inFIGS. 10, 11, and 12. The Bayesian network 650 includes a plurality ofnodes 652, 654. These nodes 652, 654 include a parent node 652 andchildren nodes 654-A through 654-E. in some embodiments, the parent node652 can identify a standard and/or a portion of a standard. In FIGS. 10,11, and 12, the parent node 652 represents the standard 3.NF.A or theportion 3.NF.A of a standard. Each of the children nodes 654-A through654-E represents items from the item bank. As seen in FIGS. 10, 11, and12, each of the nodes 652, 654 is associated with one or severalprobabilities indicative of mastery and/or of correctly responding tothe provided item. In the case of the children nodes 654-A, 654-B,654-C, 654-D, 654-E, the probability of correctly responding to aprovided item of each of these children nodes 654-A, 654-B, 654-C,654-D, 654-E can be determined based on whether the response received tothe item provided for that one of the children nodes 654-A, 654-B,654-C, 654-D, 654-E is correct or incorrect. In the case of the parentnode 652, mastery can be determined based on the conditionalprobabilities linking the parent node 652 to each of the children nodes654-A, 654-B, 654-C, 654-D, 654-E and the one or several probabilitiesof correctly responding to a provided item of each of the children nodes654-A, 654-B, 654-C, 654-D, 654-E.

In the embodiment of FIG. 10, no response has been received to any ofthe items of the children nodes 654-A, 654-B, 654-C, 654-D, 654-E, andthus the probabilities of correctly responding to a provided item of thechildren nodes 654-A, 654-B, 654-C, 654-D, 654-E are set to a defaultvalue and/or to an initial value. In some embodiments, these defaultvalues and/or initial values can be unrelated to any informationrelevant to the recipient-user such as previously gathered usermetadata, and in some embodiments, these default values and/or initialvalues can be related to any information relevant to the recipient-user.In the embodiment of FIG. 10, these default values and/or initial valuesare unrelated to previously gathered user metadata and are set to a 50%probability of correctly responding to the provided item. Due to theconditional probabilities linking the children nodes 654-A, 654-B,654-C, 654-D, 654-E and the parent node 652, in the embodiment of FIG.10, the probability of mastery of the parent node 652 is 50%.

In contrast to this situation in which no response has been received, inFIG. 11 a response has been received to the provided item of child node654-B, and specifically, a correct response has been received to theprovided item of child node 654-B. In further contrast to this situationin which no response has been received, in FIG. 12, a response has beenreceived to the child nodes 654-B and 654-E, and specifically an correctresponse has been received to both the provided item of child node 654-Band child node 654-E. Accordingly, the probability of correctlyresponding to the item associated with the child node 654-B is one or100% in both FIGS. 11 and 12, and the probability of correctlyresponding to the item associated with the child node 654-E is 1 or 100%in FIG. 12. If, in the alternative, one or both of the providedresponses were incorrect responses, the probability of correctlyresponding to the item associated with the child node associated withthat response would be 0.

With respect to FIG. 11, due to the received correct response, theupdated probability of the user correctly responding to the itemassociated with the child node 654-B, and the conditional probabilitieslinking the children nodes 654-A, 654-B, 654-C, 654-D, 654-E and theparent node 652, the probability of mastery of the parent node 652 hasincreased from 50% to 75%. Due to the conditional probabilities linkingthe parent node 652 to the other child nodes 654-A, 654-C, 654-D, 654-E,the probability of the user correctly responding to the items for eachof the other child nodes 654-A, 654-C, 654-D, 654-E has increased from50% to 62.5%. With respect to FIG. 12, due to the received correctresponses, the updated probability of the user correctly responding tothe items associated with child nodes 654-B, 654-E, and the conditionalprobabilities linking the children nodes 654-A, 654-B, 654-C, 654-D,654-E and the parent node 652, the probability of mastery of the parentnode 652 has increased, with respect to FIG. 10, from 50% to 90%, andwith respect to FIG. 11 from 75% to 90%. Due to the conditionalprobabilities linking the parent node 652 to the other child nodes654-A, 654-C, 654-D, the probability of the user correctly responding tothe items for each of the other child nodes 654-A, 654-C, 654-D hasincreased from 50% to 70% with respect to FIG. 10, and has increasedfrom 62.5% to 70% with respect to FIG. 11. Similarly, as responses toitems provided for the others of the children nodes 654-A, 654-C, 654-Dare received, the probability of mastery of the parent node 652 isupdated based on the received responses, and the conditionalprobabilities linking the children nodes 654-A, 654-B, 654-C, 654-D,654-E and the parent node 652. This updated probability can increasewhen correct responses are received, and can decrease when incorrectresponses are received.

In some embodiments, these items can all be provided to therecipient-user in a single interaction with the content distributionnetwork 100, and in some embodiments, these items can be provided to therecipient-user in a plurality of interactions with the contentdistribution network 100. In such an embodiment, for example, the itemsassociated with child nodes 654-A through 654-D may be provided to therecipient-user in a first interaction with the content distributionnetwork 100 and the items associated with child node 654-E may beprovided to the recipient-user in a second interaction.

In some embodiments, for example, this first interaction can correspondto a first interim assessment in the second interaction can correspondto a second interim assessment. In some embodiments, the first interimassessment can be provided at a first time, for example, after thecompletion of a first set of one or several instructional units, and thesecond interim assessment can be given at a second time, for example,after completion of a second set of one or several instructional units.In some embodiments, the first and second interim assessment can be usedto gather evidence of mastery of the parent node 652 without the use ofa cumulative assessment also referred to herein as a summativeassessment. In some embodiments, the use of interim assessments todetermine mastery provides several advantages over the use of cumulativeassessments. Namely, interim assessments can be provided closer in timeto when the instructional unit associated with the portions of thestandard implicated in the interim assessment were provided. This canadvantageously allow evaluating of knowledge or skill levels that arefresh in the recipient-user's mind, and can facilitate the providing ofremediation in the event that mastery is not achieve. In someembodiments, for example, because the interim assessment is providedclose to the instructional unit associated with the portions of thestandard implicated in the interim assessment, a presenter can moreeasily provide remediation or an intervention without disrupting thedelivery of content to one or several recipient-users.

A dashboard database 310 can include information for generating adashboard. In some embodiments, this information can identify one orseveral dashboard formats and/or architectures. As used herein, a formatrefers to how data is presented in a web page, and an architecturerefers to the data included in the web page and the format of that data.In some embodiments, the dashboard database 310 can comprise one orseveral pointers to other databases for retrieval of information forinclusion in the dashboard. Thus, in one embodiment, the dashboarddatabase 310 can comprise a pointer to all or portions of the userprofile database 301 to direct extraction of data from the user profiledatabase 301 for inclusion in the dashboard.

A survey database 311 may include information relating to one or severalsurveys. In some embodiments, this can include information relating tothe providing of one or several surveys and/or information gathered inresponse to one or several surveys. The information relating toproviding one or several surveys can include, for example, informationcomprising one or several surveys and/or one or several questions,information identifying one or several survey recipients including, forexample, one or several individual recipients or one or several groupsof recipients such as, for example, one or several classes or portionsof one or several classes, one or several frequencies for providingsurveys, or the like. In some embodiments, the survey database 311 caninclude information identifying when to provide a survey, whichinformation can include, for example, one or several triggers and one orseveral associated thresholds, also referred to herein as triggerthresholds. In one embodiment, these triggers comprise a plurality oftriggers delineating between circumstances in which a survey isindicated for providing and circumstances in which a survey is notindicated for providing. In some embodiments, a survey should beprovided to one or several user devices when a survey is indicated forproviding, and a survey should not be provided to one or several userdevices when a survey is not indicated for providing. In someembodiments, these one or several triggers can each be linked to one orseveral questions or surveys such that one or several questions orsurveys can be selected for providing to users based on trippedtriggers.

In some embodiments, these triggers can include, for example, a changein attendance and/or participation, including a decrease in attendanceand/or participation, an increase in attendance and/or participation,attendance and/or participation above or below a threshold level, or thelike, a change in student comprehension as indicated by a change ingrades, performance, or the like, a change in positive and/or negativereferences to a class and/or presenter in social media, or the like.

In some embodiments, the information gathered in response to the one orseveral surveys can include, for example, user provided answers to oneor several surveys, one or several survey questions, or the like. Insome embodiments, this information can be linked to the user source ofthe information, and in some embodiments, this information can beseparated from the user source of the information.

The survey information database 311 can comprise a single database or aplurality of databases such as, for example, a question database and/ora trigger database. In some embodiments, the question database caninclude a plurality of questions that can be organized according to oneor several parameters. These parameters can include, one or severalassociated triggers, one or several levels of specificity, and/or one orseveral questioned subject matter. Thus, in some embodiments, some orall of the questions in the question database can be associated with avalue linking the each of the some or all of the questions with one orseveral triggers stored in the trigger database. Further, each of thequestions can include a value associating the question with a questionedsubject matter, which question subject matter can be, for example, anarea of the course about which the question is intended to gatherinformation via student response. These areas of the course can include,for example, the presenter's teaching style (i.e. how the teacher isteaching), the appropriateness/successfulness of the course assignments,the quality and/or value of the course content, and/or the teacher'sapproach and/or interaction with one or several individuals. Thequestion database can further include one or several values identifyingthe specificity of each question in the question database. This valueidentifying specificity can result in the creation of a tree-likestructure of questions, with some trunk-questions identified as beingdirected to broad areas, and other branch-questions identified as beingdirected to one or several portions of the broad areas identified by oneor several of the trunk-questions. This tree-like structure can containmultiple levels of child-questions directed to a portion of the subjectarea of their parent questions, and these multiple levels can berepeated until a desire level of specificity is attained.

In some embodiments, the entirety of the data contained in the surveyinformation database 311 can be stored in a single memory such as, forexample, within a single memory tier, and in some embodiments, the datacontained in the survey information database 311 can be stored inmultiple memories such as, for example, within multiple tiers of memory.In some embodiments, dividing the data contained in the surveyinformation database 311 into multiple tiers of memory can allowefficient use of storage resources by placing items that are desired tobe quickly accessible in lower tiers than information that is notdesired to be as quickly accessible.

The survey database 311 can include information identifying theindividual's performance in evaluating the presenter, the course, and/orthe course material, as well as identifying the individual's performancein academic portions of the class. In some embodiments, the surveydatabase 311 includes information identifying the individual'sperformance evaluating the presenter, course, and/or the course materialand does not include information relating to the individual's academicperformance. This data may indicate the amount of time spent by theindividual in completing past surveys, indicate the number of writtencomments, or the like.

The survey database 311 can include one or several evaluations and/orevaluation reports. In some embodiments, the evaluations and/orevaluation reports can be an aggregate of data relating to presenterperformance, material performance, and/or course performance.

In some embodiments, the survey database 311 can include informationrelating to provided feedback relating to a presenter, a course, and/orlearning materials. In some embodiments, for example, this feedback caninclude one or several recommendations, including, for example, one orseveral recommended additional and/or replacement materials, one orseveral material changes, one or several recommended presenterimprovement resources such as, for example, papers, books, courses,training, seminars, or the like, which improvement resources can relateto management, organization, speaking, educational and/or instructionaltechniques, or the like.

In some embodiments, the survey database 311 can be divided into a firstportion comprising first memory components and a second portioncomprising second memory components. In some embodiments, the firstportion can comprise relatively faster memory components and the secondportion can comprise relatively slower memory components. Thus, in oneembodiment, the first portion can comprise tier 0 or tier 1 memorycomponents and the second portion can comprise tier 1 or tier 2 memorycomponents. In some embodiments, data from the survey database 311 canbe divided between the first and second portions based on whether thedata is used for real-time analysis. Thus, data used for real-timeanalysis can be stored in the first portion and data that is not usedfor real-time analysis can be stored in the second portion. In one suchembodiment a set of the triggers from the trigger database that can beused to indicate a time-sensitive desire for providing a survey can bestored within the first portion of the survey database 311, and a set ofthe triggers from the trigger database that can be used to indicate anon-time-sensitive desire for providing a survey can be stored withinthe second portion of the survey database 311.

In addition to the illustrative databases described above, databaseserver(s) 104 may include one or more external data aggregators 312.External data aggregators 312 may include third-party data sourcesaccessible to the content management network 100, but not maintained bythe content management network 100. External data aggregators 312 mayinclude any electronic information source relating to the users, contentresources, or applications of the content distribution network 100. Forexample, external data aggregators 312 may be third-party databasescontaining demographic data, education related data, consumer salesdata, health related data, and the like. Illustrative external dataaggregators 312 may include, for example, social networking web servers,public records databases, learning management systems, educationalinstitution servers, business servers, consumer sales databases, medicalrecord databases, etc. Data retrieved from various external dataaggregators 312 may be used to verify and update user accountinformation, suggest user content, and perform user and contentevaluations.

With reference now to FIG. 4, a block diagram is shown illustrating anembodiment of one or more content management servers 102 within acontent distribution network 100. As discussed above, content managementserver(s) 102 may include various server hardware and softwarecomponents that manage the content resources within the contentdistribution network 100 and provide interactive and adaptive content tousers on various user devices 106. For example, content managementserver(s) 102 may provide instructions to and receive information fromthe other devices within the content distribution network 100, in orderto manage and transmit content resources, user data, and server orclient applications executing within the network 100.

A content management server 102 may include a content customizationsystem 402. The content customization system 402 may be implementedusing dedicated hardware within the content distribution network 100(e.g., a content customization server 402), or using designated hardwareand software resources within a shared content management server 102. Insome embodiments, the content customization system 402 may adjust theselection and adaptive capabilities of content resources to match theneeds and desires of the users receiving the content. For example, thecontent customization system 402 may query various databases and servers104 to retrieve user information, such as user preferences andcharacteristics (e.g., from a user profile database 301), user accessrestrictions to content recourses (e.g., from a content access database306), previous user results and content evaluations (e.g., from anevaluation database 308), and the like. Based on the retrievedinformation from databases 104 and other data sources, the contentcustomization system 402 may modify content resources for individualusers.

In some embodiments, the content management system 402 can include arecommendation engine, also referred to herein as an adaptiverecommendation engine. In some embodiments, the recommendation enginecan select one or several pieces of content, also referred to herein asdata packets, for providing to a user. These data packets can beselected based on, for example, the information retrieved from thedatabase server 104 including, for example, the user profile database301, the content library database 303, the model database 309, or thelike. In one embodiment, for example, the recommendation engine canretrieve information from the user profile database 301 identifying, forexample, a skill level of the user. The recommendation engine canfurther retrieve information from the content library database 303identifying, for example, potential data packets for providing to theuser and the difficulty of those data packets and/or the skill levelassociated with those data packets.

The recommendation engine can use the evidence model to generate aprediction of the likelihood of one or several users providing a desiredresponse to some or all of the potential data packets. In someembodiments, the recommendation engine can pair one or several datapackets with selection criteria that may be used to determine whichpacket should be delivered to a recipient-user based on one or severalreceived responses from that recipient-user. In some embodiments, one orseveral data packets can be eliminated from the pool of potential datapackets if the prediction indicates either too high a likelihood of adesired response or too low a likelihood of a desired response. In someembodiments, the recommendation engine can then apply one or severalselection criteria to the remaining potential data packets to select adata packet for providing to the user. These one or several selectioncriteria can be based on, for example, criteria relating to a desiredestimated time for receipt of response to the data packet, one orseveral content parameters, one or several assignment parameters, or thelike.

A content management server 102 also may include a user managementsystem 404. The user management system 404 may be implemented usingdedicated hardware within the content distribution network 100 (e.g., auser management server 404), or using designated hardware and softwareresources within a shared content management server 102. In someembodiments, the user management system 404 may monitor the progress ofusers through various types of content resources and groups, such asmedia compilations, courses or curriculums in training or educationalcontexts, interactive gaming environments, and the like. For example,the user management system 404 may query one or more databases andservers 104 to retrieve user data such as associated contentcompilations or programs, content completion status, user goals,results, and the like.

A content management server 102 also may include an evaluation system406. The evaluation system 406 may be implemented using dedicatedhardware within the content distribution network 100 (e.g., anevaluation server 406), or using designated hardware and softwareresources within a shared content management server 102. The evaluationsystem 406 may be configured to receive and analyze information fromuser devices 106. For example, various ratings of content resourcessubmitted by users may be compiled and analyzed, and then stored in adatabase (e.g., a content library database 303 and/or evaluationdatabase 308) associated with the content. In some embodiments, theevaluation server 406 may analyze the information to determine theeffectiveness or appropriateness of content resources with, for example,a subject matter, an age group, a skill level, or the like. In someembodiments, the evaluation system 406 may provide updates to thecontent customization system 402 or the user management system 404, withthe attributes of one or more content resources or groups of resourceswithin the network 100. The evaluation system 406 also may receive andanalyze user evaluation data from user devices 106, supervisor devices110, and administrator servers 116, etc. For instance, evaluation system406 may receive, aggregate, and analyze user evaluation data fordifferent types of users (e.g., end users, supervisors, administrators,etc.) in different contexts (e.g., media consumer ratings, trainee orstudent comprehension levels, presenter effectiveness levels, gamerskill levels, etc.).

In some embodiments, the evaluation system 406 can be further configuredto receive one or several responses from the user and to determinewhether the one or several response are correct responses, also referredto herein as desired responses, or are incorrect responses, alsoreferred to herein as undesired responses. In some embodiments, one orseveral values can be generated by the evaluation system 406 to reflectuser performance in responding to the one or several data packets. Insome embodiments, these one or several values can comprise one orseveral scores for one or several responses and/or data packets.

The evaluation system 406 can utilize a scoring engine to evaluate theone or several received responses and/or to determine whether the one orseveral responses are correct response. The scoring engine can, in someembodiments, generate a score according to information contained in, forexample, the database server 104, and specifically within the evaluationdatastore 308. In some embodiments, a response vector can be generated,by, for example, the evaluation system 406, for each received response.The evaluation system 406 can determine the correctness and/or theincorrectness of the received response via an evaluation of the responsevector. The response vector can, in some embodiments, be updatedsubsequent to evaluation of the associated response to includeinformation relating to the evaluated response.

In some embodiments, the scoring engine can comprise a statisticallearning model, which can, in some embodiments, be a machine learningmodel, that can predict a user skill level for a portion of content suchas, for example, for a content domain. Specifically, in someembodiments, the statistical learning model can comprise a onedimensional model, also referred to herein as a unidimensional model.The unidimensional model can predict a skill level of a user within acontent domain based on received and/or evaluated responses of that userto questions from that content domain. In some embodiments, for example,while an assessment is being given, one or several questions can relateto a content domain, such as a first content domain. A content domaincan correspond to a subset of content corresponding to an object at alevel in the hierarchical data structure.

These one or several questions relating to the first content domain canbe provided to the user and responses can be received to thosequestions. The unidimensional model can evaluate the received response,which can include the generation of a response vector that characterizesthe received response, and in some embodiments, characterizes theevaluation of the received response. In some embodiments, a responsevector can be generated for each of the received responses. The responsevectors for the first content domain can be ingested by theunidimensional model, and the unidimensional model can output apredicted skill level within the first content domain based on theseingested response vectors.

In some embodiments, the user can progress through multiple contentdomains during the assessment, and the unidimensional model can be usedto predict a skill level for the user for each of the content domains asthese content domains are completed, or in other words, as terminationcriteria have been met for each of the content domains. Thus, in someembodiments, a user can receive questions from a content domain untilthe termination criteria for that content domain are met. In someembodiments, after each response is received, the received response canbe evaluated and the user skill level can be estimated. Based on thereceived response and/or the user skill level, it can be determined whenthe content domain is completed, at which time, the user can progress toa next content domain, and the unidimensional model can predict userskill levels for that next content domain as responses to questions fromthat content domain are received. This can be repeated until one orseveral termination criteria for the assessment have been reached, atwhich point a multidimensional model can be triggered.

The multidimensional model can generate a predicted user skill level fora plurality levels within the hierarchical data structure and cangenerate this prediction based on inputs from multiple content domains.In some embodiments, for example, the multidimensional model can ingestdata indicative of the user's skill level in the content domains of anassessment and can output an estimated skill level based on theassessment. In some embodiments, for example, the multidimensional modelcan ingest response vectors for responses received in multiple contentdomains in the assessment and/or scalar skill levels for content domainsin the assessment, and can output an estimated skill level for theassessment. In some embodiments, the multidimensional model can ingestinputs, such as response vectors, associated with responses receivedfrom the user, but that are unrelated to the assessment.

Thus, in embodiments in which both the unidimensional model and themultidimensional model are used, a first evaluation engine can generatescalar skill level estimate during the assessment and a secondevaluation engine can generate a vector skill level estimate uponcompletion of the assessment.

A content management server 102 also may include a content deliverysystem 408. The content delivery system 408 may be implemented usingdedicated hardware within the content distribution network 100 (e.g., acontent delivery server 408), or using designated hardware and softwareresources within a shared content management server 102. The contentdelivery system 408 can include a presentation engine that can be, forexample, a software module running on the content delivery system.

The content delivery system 408, also referred to herein as thepresentation module or the presentation engine, may receive contentresources from the content customization system 402 and/or from the usermanagement system 404, and provide the resources to user devices 106.The content delivery system 408 may determine the appropriatepresentation format for the content resources based on the usercharacteristics and preferences, and/or the device capabilities of userdevices 106. If needed, the content delivery system 408 may convert thecontent resources to the appropriate presentation format and/or compressthe content before transmission. In some embodiments, the contentdelivery system 408 may also determine the appropriate transmissionmedia and communication protocols for transmission of the contentresources.

In some embodiments, the content delivery system 408 may includespecialized security and integration hardware 410, along withcorresponding software components to implement the appropriate securityfeatures content transmission and storage, to provide the supportednetwork and client access models, and to support the performance andscalability requirements of the network 100. The security andintegration layer 410 may include some or all of the security andintegration components 208 discussed above in FIG. 2, and may controlthe transmission of content resources and other data, as well as thereceipt of requests and content interactions, to and from the userdevices 106, supervisor devices 110, administrative servers 116, andother devices in the network 100.

With reference now to FIG. 5, a flowchart illustrating one embodiment ofa process 440 for data management is shown. In some embodiments, theprocess 440 can be performed by the content management server 102, andmore specifically by the content delivery system 408 and/or by thepresentation module or presentation engine. The process 440 begins atblock 442, wherein a data packet is identified. In some embodiments, thedata packet can be a data packet for providing to a recipient-user, andthe data packet can be identified by determining which data packet tonext provide to the user such as the recipient-user. In someembodiments, this determination can be performed by the contentcustomization system 402 and/or the recommendation engine.

After the data packet has been identified, the process 440 proceeds toblock 444, wherein the data packet is requested. In some embodiments,this can include the requesting of information relating to the datapacket such as the data forming the data packet. In some embodiments,this information can be requested from, for example, the content librarydatabase 303. After the data packet has been requested, the process 440proceeds to block 446, wherein the data packet is received. In someembodiments, the data packet can be received by the content deliverysystem 408 from, for example, the content library database 303.

After the data packet has been received, the process 440 proceeds toblock 448, wherein one or several data components are identified. Insome embodiments, for example, the data packet can include one orseveral data components which can, for example, contain different data.In some embodiments, one of these data components, referred to herein asa presentation component, can include content for providing to thestudent user, which content can include one or several requests and/orquestions and/or the like. In some embodiments, one of these datacomponents, referred to herein as a response component, can include dataused in evaluating one or several responses received from the userdevice 106 in response to the data packet, and specifically in responseto the presentation component and/or the one or several requests and/orquestions of the presentation component. Thus, in some embodiments, theresponse component of the data packet can be used to ascertain whetherthe user has provided a desired response or an undesired response.

After the data components have been identified, the process 440 proceedsto block 450, wherein a delivery data packet is identified. In someembodiments, the delivery data packet can include the one or severaldata components of the data packets for delivery to a user such as therecipient-user via the user device 106. In some embodiments, thedelivery packet can include the presentation component, and in someembodiments, the delivery packet can exclude the response packet. Afterthe delivery data packet has been generated, the process 440 proceeds toblock 452, wherein the delivery data packet is presented to the userdevice 106. In some embodiments, this can include providing the deliverydata packet to the user device 106 via, for example, the communicationnetwork 120.

After the delivery data packet has been provided to the user device, theprocess 440 proceeds to block 454, wherein the data packet and/or one orseveral components thereof is sent to and/or provided to the responseprocessor. In some embodiments, this sending of the data packet and/orone or several components thereof to the response processor can includereceiving a response from the recipient-user, and sending the responseto the recipient-user to the response processor simultaneous with thesending of the data packet and/or one or several components thereof tothe response processor. In some embodiments, for example, this caninclude providing the response component to the response processor. Insome embodiments, the response component can be provided to the responseprocessor from the content delivery system 408.

With reference now to FIG. 6, a flowchart illustrating one embodiment ofa process 460 for evaluating a response is shown. In some embodiments,the process can be performed by the evaluation system 406. In someembodiments, the process 460 can be performed by the evaluation system406 in response to the receipt of a response from the user device 106.

The process 460 begins at block 462, wherein a response is receivedfrom, for example, the user device 106 via, for example, thecommunication network 120. After the response has been received, theprocess 460 proceeds to block 464, wherein the data packet associatedwith the response is received. In some embodiments, this can includereceiving all or one or several components of the data packet such as,for example, the response component of the data packet. In someembodiments, the data packet can be received by the response processorfrom the presentation engine.

After the data packet has been received, the process 460 proceeds toblock 466, wherein the response type is identified. In some embodiments,this identification can be performed based on data, such as metadataassociated with the response. In other embodiments, this identificationcan be performed based on data packet information such as the responsecomponent.

In some embodiments, the response type can identify one or severalattributes of the one or several requests and/or questions of the datapacket such as, for example, the request and/or question type. In someembodiments, this can include identifying some or all of the one orseveral requests and/or questions as true/false, multiple choice, shortanswer, essay, or the like.

After the response type has been identified, the process 460 proceeds toblock 468, wherein the data packet and the response are compared todetermine whether the response comprises a desired response and/or anundesired response. In some embodiments, this can include comparing thereceived response and the data packet to determine if the receivedresponse matches all or portions of the response component of the datapacket, to determine the degree to which the received response matchesall or portions of the response component, to determine the degree towhich the receive response embodies one or several qualities identifiedin the response component of the data packet, or the like. In someembodiments, this can include classifying the response according to oneor several rules. In some embodiments, these rules can be used toclassify the response as either desired or undesired. In someembodiments, these rules can be used to identify one or several errorsand/or misconceptions evidenced in the response. In some embodiments,this can include, for example: use of natural language processingsoftware and/or algorithms; use of one or several digital thesauruses;use of lemmatization software, dictionaries, and/or algorithms; or thelike.

After the data packet and the response have been compared, the process460 proceeds to block 470 wherein response desirability is determined.In some embodiments this can include, based on the result of thecomparison of the data packet and the response, whether the response isa desired response or is an undesired response. In some embodiments,this can further include quantifying the degree to which the response isa desired response. This determination can include, for example,determining if the response is a correct response, an incorrectresponse, a partially correct response, or the like. In someembodiments, the determination of response desirability can include thegeneration of a value characterizing the response desirability and thestoring of this value in one of the databases 104 such as, for example,the user profile database 301. After the response desirability has beendetermined, the process 460 proceeds to block 472, wherein an assessmentvalue is generated. In some embodiments, the assessment value can be anaggregate value characterizing response desirability for one or more aplurality of responses. This assessment value can be stored in one ofthe databases 104 such as the user profile database 301.

With reference now to FIG. 7, a block diagram of an illustrativecomputer system is shown. The system 500 may correspond to any of thecomputing devices or servers of the content distribution network 100described above, or any other computing devices described herein, andspecifically can include, for example, one or several of the userdevices 106, the supervisor device 110, and/or any of the servers 102,104, 108, 112, 114, 116. In this example, computer system 500 includesprocessing units 504 that communicate with a number of peripheralsubsystems via a bus subsystem 502. These peripheral subsystems include,for example, a storage subsystem 510, an I/O subsystem 526, and acommunications subsystem 532.

Bus subsystem 502 provides a mechanism for letting the variouscomponents and subsystems of computer system 500 communicate with eachother as intended. Although bus subsystem 502 is shown schematically asa single bus, alternative embodiments of the bus subsystem may utilizemultiple buses. Bus subsystem 502 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Sucharchitectures may include, for example, an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P1386.1standard.

Processing unit 504, which may be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 500. One or more processors,including single core and/or multicore processors, may be included inprocessing unit 504. As shown in the figure, processing unit 504 may beimplemented as one or more independent processing units 506 and/or 508with single or multicore processors and processor caches included ineach processing unit. In other embodiments, processing unit 504 may alsobe implemented as a quad-core processing unit or larger multicoredesigns (e.g., hexa-core processors, octo-core processors, ten-coreprocessors, or greater.

Processing unit 504 may execute a variety of software processes embodiedin program code, and may maintain multiple concurrently executingprograms or processes. At any given time, some or all of the programcode to be executed can be resident in processor(s) 504 and/or instorage subsystem 510. In some embodiments, computer system 500 mayinclude one or more specialized processors, such as digital signalprocessors (DSPs), outboard processors, graphics processors,application-specific processors, and/or the like.

I/O subsystem 526 may include device controllers 528 for one or moreuser interface input devices and/or user interface output devices 530.User interface input and output devices 530 may be integral with thecomputer system 500 (e.g., integrated audio/video systems, and/ortouchscreen displays), or may be separate peripheral devices which areattachable/detachable from the computer system 500. The I/O subsystem526 may provide one or several outputs to a user by converting one orseveral electrical signals to user perceptible and/or interpretableform, and may receive one or several inputs from the user by generatingone or several electrical signals based on one or several user-causedinteractions with the I/O subsystem such as the depressing of a key orbutton, the moving of a mouse, the interaction with a touchscreen ortrackpad, the interaction of a sound wave with a microphone, or thelike.

Input devices 530 may include a keyboard, pointing devices such as amouse or trackball, a touchpad or touch screen incorporated into adisplay, a scroll wheel, a click wheel, a dial, a button, a switch, akeypad, audio input devices with voice command recognition systems,microphones, and other types of input devices. Input devices 530 mayalso include three dimensional (3D) mice, joysticks or pointing sticks,gamepads and graphic tablets, and audio/visual devices such as speakers,digital cameras, digital camcorders, portable media players, webcams,image scanners, fingerprint scanners, barcode reader 3D scanners, 3Dprinters, laser rangefinders, and eye gaze tracking devices. Additionalinput devices 530 may include, for example, motion sensing and/orgesture recognition devices that enable users to control and interactwith an input device through a natural user interface using gestures andspoken commands, eye gesture recognition devices that detect eyeactivity from users and transform the eye gestures as input into aninput device, voice recognition sensing devices that enable users tointeract with voice recognition systems through voice commands, medicalimaging input devices, MIDI keyboards, digital musical instruments, andthe like.

Output devices 530 may include one or more display subsystems, indicatorlights, or non-visual displays such as audio output devices, etc.Display subsystems may include, for example, cathode ray tube (CRT)displays, flat-panel devices, such as those using a liquid crystaldisplay (LCD) or plasma display, light-emitting diode (LED) displays,projection devices, touch screens, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system500 to a user or other computer. For example, output devices 530 mayinclude, without limitation, a variety of display devices that visuallyconvey text, graphics and audio/video information such as monitors,printers, speakers, headphones, automotive navigation systems, plotters,voice output devices, and modems.

Computer system 500 may comprise one or more storage subsystems 510,comprising hardware and software components used for storing data andprogram instructions, such as system memory 518 and computer-readablestorage media 516. The system memory 518 and/or computer-readablestorage media 516 may store program instructions that are loadable andexecutable on processing units 504, as well as data generated during theexecution of these programs.

Depending on the configuration and type of computer system 500, systemmemory 318 may be stored in volatile memory (such as random accessmemory (RAM) 512) and/or in non-volatile storage drives 514 (such asread-only memory (ROM), flash memory, etc.) The RAM 512 may contain dataand/or program modules that are immediately accessible to and/orpresently being operated and executed by processing units 504. In someimplementations, system memory 518 may include multiple different typesof memory, such as static random access memory (SRAM) or dynamic randomaccess memory (DRAM). In some implementations, a basic input/outputsystem (BIOS), containing the basic routines that help to transferinformation between elements within computer system 500, such as duringstart-up, may typically be stored in the non-volatile storage drives514. By way of example, and not limitation, system memory 518 mayinclude application programs 520, such as client applications, Webbrowsers, mid-tier applications, server applications, etc., program data522, and an operating system 524.

Storage subsystem 510 also may provide one or more tangiblecomputer-readable storage media 516 for storing the basic programmingand data constructs that provide the functionality of some embodiments.Software (programs, code modules, instructions) that when executed by aprocessor provide the functionality described herein may be stored instorage subsystem 510. These software modules or instructions may beexecuted by processing units 504. Storage subsystem 510 may also providea repository for storing data used in accordance with the presentinvention.

Storage subsystem 300 may also include a computer-readable storage mediareader that can further be connected to computer-readable storage media516. Together and, optionally, in combination with system memory 518,computer-readable storage media 516 may comprehensively representremote, local, fixed, and/or removable storage devices plus storagemedia for temporarily and/or more permanently containing, storing,transmitting, and retrieving computer-readable information.

Computer-readable storage media 516 containing program code, or portionsof program code, may include any appropriate media known or used in theart, including storage media and communication media, such as but notlimited to, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible computer-readable storagemedia such as RAM, ROM, electronically erasable programmable ROM(EEPROM), flash memory or other memory technology, CD-ROM, digitalversatile disk (DVD), or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or other tangible computer readable media. This can also includenontangible computer-readable media, such as data signals, datatransmissions, or any other medium which can be used to transmit thedesired information and which can be accessed by computer system 500.

By way of example, computer-readable storage media 516 may include ahard disk drive that reads from or writes to non-removable, nonvolatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, nonvolatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, nonvolatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 516 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 516 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for computer system 500.

Communications subsystem 532 may provide a communication interface fromcomputer system 500 and external computing devices via one or morecommunication networks, including local area networks (LANs), wide areanetworks (WANs) (e.g., the Internet), and various wirelesstelecommunications networks. As illustrated in FIG. 7, thecommunications subsystem 532 may include, for example, one or morenetwork interface controllers (NICs) 534, such as Ethernet cards,Asynchronous Transfer Mode NICs, Token Ring NICs, and the like, as wellas one or more wireless communications interfaces 536, such as wirelessnetwork interface controllers (WNICs), wireless network adapters, andthe like. As illustrated in FIG. 7, the communications subsystem 532 mayinclude, for example, one or more location determining features 538 suchas one or several navigation system features and/or receivers, and thelike. Additionally and/or alternatively, the communications subsystem532 may include one or more modems (telephone, satellite, cable, ISDN),synchronous or asynchronous digital subscriber line (DSL) units,FireWire® interfaces, USB® interfaces, and the like. Communicationssubsystem 536 also may include radio frequency (RF) transceivercomponents for accessing wireless voice and/or data networks (e.g.,using cellular telephone technology, advanced data network technology,such as 3G, 4G or EDGE (enhanced data rates for global evolution), WiFi(IEEE 802.11 family standards, or other mobile communicationtechnologies, or any combination thereof), global positioning system(GPS) receiver components, and/or other components.

The various physical components of the communications subsystem 532 maybe detachable components coupled to the computer system 500 via acomputer network, a FireWire® bus, or the like, and/or may be physicallyintegrated onto a motherboard of the computer system 500. Communicationssubsystem 532 also may be implemented in whole or in part by software.

In some embodiments, communications subsystem 532 may also receive inputcommunication in the form of structured and/or unstructured data feeds,event streams, event updates, and the like, on behalf of one or moreusers who may use or access computer system 500. For example,communications subsystem 532 may be configured to receive data feeds inreal-time from users of social networks and/or other communicationservices, web feeds such as Rich Site Summary (RSS) feeds, and/orreal-time updates from one or more third party information sources(e.g., data aggregators 312). Additionally, communications subsystem 532may be configured to receive data in the form of continuous datastreams, which may include event streams of real-time events and/orevent updates (e.g., sensor data applications, financial tickers,network performance measuring tools, clickstream analysis tools,automobile traffic monitoring, etc.). Communications subsystem 532 mayoutput such structured and/or unstructured data feeds, event streams,event updates, and the like to one or more databases 104 that may be incommunication with one or more streaming data source computers coupledto computer system 500.

Due to the ever-changing nature of computers and networks, thedescription of computer system 500 depicted in the figure is intendedonly as a specific example. Many other configurations having more orfewer components than the system depicted in the figure are possible.For example, customized hardware might also be used and/or particularelements might be implemented in hardware, firmware, software, or acombination. Further, connection to other computing devices, such asnetwork input/output devices, may be employed. Based on the disclosureand teachings provided herein, a person of ordinary skill in the artwill appreciate other ways and/or methods to implement the variousembodiments.

With reference now to FIG. 8, a block diagram illustrating oneembodiment of the communication network is shown. Specifically, FIG. 8depicts one hardware configuration in which messages are exchangedbetween a source hub 602 via the communication network 120 that caninclude one or several intermediate hubs 604. In some embodiments, thesource hub 602 can be any one or several components of the contentdistribution network generating and initiating the sending of a message,and the terminal hub 606 can be any one or several components of thecontent distribution network 100 receiving and not re-sending themessage. In some embodiments, for example, the source hub 602 can be oneor several of the user device 106, the supervisor device 110, and/or theserver 102, and the terminal hub 606 can likewise be one or several ofthe user device 106, the supervisor device 110, and/or the server 102.In some embodiments, the intermediate hubs 604 can include any computingdevice that receives the message and resends the message to a next node.

As seen in FIG. 8, in some embodiments, each of the hubs 602, 604, 606can be communicatingly connected with the data store 104. In such anembodiments, some or all of the hubs 602, 604, 606 can send informationto the data store 104 identifying a received message and/or any sent orresent message. This information can, in some embodiments, be used todetermine the completeness of any sent and/or received messages and/orto verify the accuracy and completeness of any message received by theterminal hub 606.

In some embodiments, the communication network 120 can be formed by theintermediate hubs 604. In some embodiments, the communication network120 can comprise a single intermediate hub 604, and in some embodiments,the communication network 120 can comprise a plurality of intermediatehubs. In one embodiment, for example, and as depicted in FIG. 68, thecommunication network 120 includes a first intermediate hub 604-A and asecond intermediate hub 604-B.

With reference now to FIG. 9, a block diagram illustrating oneembodiment of user device 106 and supervisor device 110 communication isshown. In some embodiments, for example, a user may have multipledevices that can connect with the content distribution network 100 tosend or receive information. In some embodiments, for example, a usermay have a personal device such as a mobile device, a Smartphone, atablet, a Smartwatch, a laptop, a PC, or the like. In some embodiments,the other device can be any computing device in addition to the personaldevice. This other device can include, for example, a laptop, a PC, aSmartphone, a tablet, a Smartwatch, or the like. In some embodiments,the other device differs from the personal device in that the personaldevice is registered as such within the content distribution network 100and the other device is not registered as a personal device within thecontent distribution network 100.

Specifically with respect to FIG. 9, the user device 106 can include apersonal user device 106-A and one or several other user devices 106-B.In some embodiments, one or both of the personal user device 106-A andthe one or several other user devices 106-B can be communicatinglyconnected to the content management server 102 and/or to the navigationsystem 122. Similarly, the supervisor device 110 can include a personalsupervisor device 110-A and one or several other supervisor devices110-B. In some embodiments, one or both of the personal supervisordevice 110-A and the one or several other supervisor devices 110-B canbe communicatingly connected to the content management server 102 and/orto the navigation system 122.

In some embodiments, the content distribution network can send one ormore alerts to one or more user devices 106 and/or one or moresupervisor devices 110 via, for example, the communication network 120.In some embodiments, the receipt of the alert can result in thelaunching of an application within the receiving device, and in someembodiments, the alert can include a link that, when selected, launchesthe application or navigates a web-browser of the device of the selectorof the link to page or portal associated with the alert.

In some embodiments, for example, the providing of this alert caninclude the identification of one or several user devices 106 and/orrecipient-user accounts associated with the recipient-user and/or one orseveral supervisor devices 110 and/or supervisor-user accountsassociated with the supervisor-user. After these one or several devices106, 110 and/or accounts have been identified, the providing of thisalert can include determining an active device of the devices 106, 110based on determining which of the devices 106, 110 and/or accounts areactively being used, and then providing the alert to that active device.

Specifically, if the user is actively using one of the devices 106, 110such as the other user device 106-B and the other supervisor device110-B, and/or accounts, the alert can be provided to the user via thatother device 106-B, 110-B and/or account that is actively being used. Ifthe user is not actively using an other device 106-B, 110-B and/oraccount, a personal device 106-A, 110-A device, such as a smart phone ortablet, can be identified and the alert can be provided to this personaldevice 106-A, 110-A. In some embodiments, the alert can include code todirect the recipient device to provide an indicator of the receivedalert such as, for example, an aural, tactile, or visual indicator ofreceipt of the alert.

In some embodiments, the recipient device 106, 110 of the alert canprovide an indication of receipt of the alert. In some embodiments, thepresentation of the alert can include the control of the I/O subsystem526 to, for example, provide an aural, tactile, and/or visual indicatorof the alert and/or of the receipt of the alert. In some embodiments,this can include controlling a screen of the supervisor device 110 todisplay the alert, data contained in alert and/or an indicator of thealert.

With reference now to FIG. 13, an exemplary embodiment of a hierarchicaldata structure 800 is shown. The hierarchical data structure 800 caninclude a plurality of levels 802. These levels can include a firstlevel 802-A corresponding to a subject level, a second level 802-Bcorresponding to a domain level, a third level 802-C corresponding to acluster level, a fourth level 802-D corresponding to a standard level,and a fifth level 802-E corresponding to a sub-standard level. In someembodiments, the hierarchical data structure 800 can include more orfewer levels 802 than those specifically depicted in FIG. 13.

The levels 802 can be arranged in a hierarchical relationship such thatin a pair of adjacent levels, one level is a parent level and the otheris a child level. With specific reference to FIG. 13, the first level802-A is a parent to the second-level 802-B, the second-level 802-B is aparent to the third level 802-C, the third level 802-C is a parent tothe fourth level 802-D, and the fourth level 802-D is a parent to thefifth level 802-E.

In some embodiments, each level 802 can comprise one or severalcategories 804. In identifying relationships between levels andcategories, a category in a parent level is identified as a parentcategory and a category in a child level is identified as a childcategory. Relationships between parent and child categories areindicated by arrows, some of which arrows are identified as 806 in FIG.13. However, as some or all levels can be a parent level with responseto one level and a child level with respect to another level,identification of a category 804 as a parent category or child categoryis not absolute, but is dependent on relative positioning of thecategory with respect to other categories. This is illustrated withreference FIG. 13. As seen, the second-level 802-B includes category804-A which is a parent category to child category 804-B located in thethird level 802-C. While category 804-B is a child category to category804-A, category 804-B is a parent category to categories 804-C and80-4-D.

As used herein, a parent category is upstream coupled from a childcategory, and a child category is downstream coupled from a parentcategory. Thus, category 804-B is downstream coupled to category 804-A,and category 804-A is upstream coupled from category 804-B.

In some embodiments, each of these categories 804 of a level 802 cancomprise a weighting value. The weighting value of a category 804identifies the relative contribution of that category 804 to a parentcategory in the parent level to which the category associated with thatweighting value is linked. To facilitate discussions, illustrativeweighting values 808 of categories 804-B, 804-C, 804-D, 804-E areillustrated in FIG. 13.

In the illustrative embodiment of FIG. 13, the sum of all weightingvalues of all child categories of a parent category is equal to 1. Thus,the sum of the weighting values of categories 804-C and 804-D is 1, andthe sum of weighting values 808 of categories 804-B and 804-E is 1. Insome embodiments, the weighting value of an category can be translatedto parent or child categories. By way of example, and with references tocategories 804-A, 804-B, 804-C, 804-D, category 804-C is linked tocategory 804-A via path 810-A represented by arrows 806-A and 806-B, andcategory 804-D is linked to category 805-A via path 810-B represented byarrows 806-A and 806-C. The weighting value 808 of category 804-C can betranslated to category 804-A by identifying intermediate weightingvalues—such as weighting value 808 of category 804-B—and combining theseweighting values. Specifically, the weighting value 808 of category804-C can be translated to a weighting value of category 804-A bymultiplying the weighting value 808 of category 804-C with the weightingvalue 808 of category 804-B.

This translation can be modified for purposes of an assessment, and fordetermining a normalized weighting value when an assessment does notinclude all child categories of a parent category. In such anembodiment, the weighting values 808 of child categories can benormalized by, for example, determining the sum of weighting values ofall child categories of the parent category that are included in theassessment and dividing each of the weighting values 808 of the childcategories by the sum of weighting values of all child categories of theparent category that are included in the assessment.

With reference now to FIG. 14, a schematic illustration of oneembodiment of a creation interface 820 is shown. The creation interface820 can be used in the creation of an assessment. Specifically, thecreation interface 820 can enable a user to select one or severalcontent categories for inclusion in the assessment, which contentcategories can be displayed in the creation interface according to thehierarchical data structure 800. The creation interface 820 can storeuser inputs received selecting one or several content categories and cangenerate the assessment based on those selections.

The creation interface 820 comprises a display window 822 that includesa plurality of objects, each object representing a portion of thehierarchical data structure and more specifically each objectrepresenting a category within the hierarchical data structure 800. Thehierarchical data structure 800 is reflected in the nesting of someobjects within other objects, and specifically the nesting of someobjects within boundaries of other objects. In some embodiments, forexample, one or several objects representing child categories are nestedwithin the boundary of an object representing the parent category ofthose child categories.

The creation interface 820 includes one or several subject windows 824that define the category corresponding to the first level 802-A in thehierarchical data structure 800. Specifically, the creation interface820 of FIG. 14 includes a first subject window 824-A identifying a topicof the first level 802-A and a second subject window 824-B identifyinggrade level of the topic identified in window 824-A. The display windowfurther includes a type window 826, wherein a type of the assessmentbeing created with the creation interface 820 is shown. In someembodiments, the assessment type can be, for example, adaptive ornon-adaptive. In some embodiments, and adaptive-type assessment caninclude the adaptive selection of next items and/or questions accordingto a test-taker skill level determined based on one or several receiveduser responses.

The creation interface 820 further includes objects 828 corresponding tothe categories and/or levels within the hierarchical data structure 800.These objects 828 include one or several first objects 830, each ofwhich first objects 830 corresponding to one category in the secondlevel 802-B in the hierarchical data structure 800. Specifically, thesefirst objects 830 include: first object 830-A corresponding to a firstcategory of the second level 802-B; first object 830-B corresponding tothe a second category of the second level 802-B; first objective 830-Ccorresponding to a third category of the second level 802-B; firstobject 830-D corresponding to a fourth category of the second level802-B; and first objective 830-E corresponding to the fifth category ofthe second level 802-B.

The objects 828 include one or several second objects 832, each of whichsecond objects 832 corresponds to one category in the third level 802-Cof the hierarchical data structure 800. Second object 832-A correspondsto a first category in the third level 802-C, second object 832-Bcorresponds to a second category in the third level 802-C, second object832-C corresponds to a third category in the third level 802-C, andsecond object 832-D corresponds to a fourth category in the third level802-C. In the embodiment of FIG. 14, first object 830-A includes foursecond objects 832-A, 832-B, 832-C, 832-D nested within a boundary 831of first object 830-A.

The objects 828 include one or several third objects 834, each of whichthird objects 834 corresponds to one category in the fourth level 802-Dof the hierarchical data structure 800. Third object 834-A correspond toa first category in the fourth level 802-D, third object 834-Bcorresponds to a second category in the fourth level 802-D, third object834-C corresponds to a third category in the fourth level 802-D, andthird object 834-D corresponds to a fourth category in the fourth level802-D. In the embodiment of FIG. 14, second object 832-A includes fourthird objects 834 nested within a boundary 833 of the second object832-A.

The creation interface 820 can include objects corresponding levels inhierarchical data structure 800. In embodiments in which thehierarchical data structure 800 has more levels than those shown in FIG.13, the creation interface 820 can include further nestings of objectsthan shown in FIG. 14. In embodiments in which the hierarchical datastructure 800 has fewer levels than those shown in FIG. 13, the creationinterface 820 can include fewer nestings of objects than shown in FIG.14.

In some embodiments, the creation interface 820 can be provided to auser to facilitate in the creation of an assessment. The user can selectone or several of the objects 828 displayed in the creation interface820 to designate the selected one or several objects 828 for inclusionin the assessment being created. In some embodiments, the creationinterface 820 can be provided to a user via a touch screen, and the usercan select one or several objects 828 via touching of the touch screenat the positions on the touch screen at which those one or severalobjects 828 are displayed. As an object is selected, the creationinterface 820 can modify the display of the selected objects 828 toallow quick identification of selected objects 828. This modification tothe display can include, for example, a change in the color, style,shape, or the like of the selected object 828.

The creation interface 820 further includes a manipulable feature suchas build button 836, the manipulation of which stores received objectselections and triggers generation of a confirmation interface.

With reference now to FIG. 15, a schematic illustration of oneembodiment of the confirmation interface 840 is shown. The confirmationinterface 840 can be generated subsequent to the manipulation of themanipulable feature such as the manipulation of the build button 836 inthe creation interface 820 and/or in response to receipt of selection ofone or several objects in the creation interface 820. The confirmationinterface 840 provides a display of the objects selected in the creationinterface 820, as well as of the nesting of those selected objects intoother objects in the creation interface 820. This display of theselected objects, as well as of the nesting of the selected objectsprovides a readily understandable indication of the position of theselected objects within the hierarchical data structure 800 and of thecontribution of the selected objects to mastery of content within thehierarchical data structure 800.

As indicated in FIG. 15, the confirmation interface 840 includes adisplay window 842 that includes a display of selected objects 844,which selected objects 844 can be some or all of the objects 828 fromthe creation interface 820. The selected objects 844 can include one orseveral first objects 830, one or several second objects 832, and/or oneor several third objects 834. The confirmation interface 840 furtherincludes a scheduling button 846 which can comprise a manipulablefeature. When the scheduling button 846 is manipulated, the assessmentis finalized and/or scheduled based on objects confirmed as selected inthe confirmation interface 840.

In some embodiments, the confirmation interface 840 can be used tomodify selection of one or more objects from the creation interface 820.In some embodiments, for example, one or several objects selected in thecreation interface 820 can be unselected in the confirmation interface840 and/or one or several objects that were not previously selected inthe creation interface 820 can be selected in the confirmation interface840. Upon confirmation of content selection via the confirmationinterface 840, the server 102 can create the assessment with contentassociated with the selected objects and/or can schedule the assessment.

With reference now to FIG. 16, a schematic illustration of oneembodiment of an assessment generation and delivery system 850 is shown.The delivery system 850 can be a part of the CDN 100 and/or can includeoverlapping components with the CDN 100. The delivery system 850 can beconfigured to generate an assessment with the generation module 854based on user inputs received via the interface 852, and to deliver theassessment to a user via the delivery module 856. In some embodiments,the interface 852, the generation module 854, and the delivery module856 can be hardware and/or software modules located within the server102. In some embodiments, for example, the interface module 852 and/orthe generation module 854 can be embodied within the contentcustomization system 402 and/or can be component or module of the same.The delivery module 856 can, in some embodiments, be embodied within thecontent delivery system 408 and/or can be embodied within the contentdelivery system 408.

In some embodiments, and as depicted in FIG. 16, the user device 106 cancommunicate with one or both of the interface 852 and the deliverymodule 856, and the data store 104 can communicate with one or both ofthe generation module 854 and the delivery module 856. In someembodiments, for example, the interface module 852 directs thegeneration of the creation interface 820 and/or the confirmationinterface 840 on the user device 106, and receives inputs from the userdevice 106 selecting one or several objects and confirming the selectionof the same. The selected and confirmed objects are provided to thegeneration module 854, which retrieves metadata associated with each ofthe selected objectives from the database server 104, and specifically,retrieves weighting values associated with the selected objectives. Thegeneration module 854 can determine normalized weighting values for theselected objects based on the retrieved weighting values, and can usethese normalized weighting values in the selection of items forinclusion in the assessment and/or for the defining assessmentcomposition guidelines vis-à-vis items associated with selectedobjectives. After the assessment is generated, the delivery module 856can deliver the assessment to a user device 106, which user device canbe the same user device 106 or can be a different user device 106 thanthe user device 106 used to create the assessment. In some embodiments,regardless of whether the user device 106 is the same, the assessmentcan be created based on inputs from a first user, such as a teacher, andthe assessment can be provided to one or several second users, such asone or several student. Based on responses received from the user, nextcontent can be selected and presented by the delivery module 856, anduser skill levels can be estimated. In some embodiments, the user skilllevel can be estimated upon completion of the assessment, and can be avector skill level.

With reference now FIG. 17, a flowchart illustrating one embodiment of aprocess 860 for automated assessment generation is shown. The process860 can be performed by the processor 102, and specifically, can beperformed by the interface 852, the generation module 854, and thedelivery module 856, in combination with the database server 104. Theprocess 860 includes a first component can be performed by the interface852, second component can be performed by the generation module 854, andthe third component can be performed by the delivery module 856.

The process 860 begins at block 862 wherein the creation interface 820,is generated. In some embodiments, the generation of the creationinterface 820 can include the generation of one or several controlsignals by the server 102 and specifically by the interface module 402of the server. These one or several control signals can direct the userdevice 106 to generate and display the creation interface 820. In someembodiments, these control signals can specify the generation of some orall of the features of the creation interface 820 as shown in FIG. 14,including the generation of the objects 828. In some embodiments, theassessment creation interface 820 can include a plurality of nestedobjects each representative of a portion of the hierarchical datastructure 800. In some embodiments, the generation of the creationinterface 820 can be performed by the interface 852.

And block 864, one or several user selections are received via thecreation interface 820. In some embodiments, these user selections caninclude the selection of one or several objects 828 for inclusion in theassessment being generated. In some embodiments, for example, a user canselect at least a first object in the second object for inclusion in theassessment being generated. In some embodiments, the first object in thesecond object can be selected from the plurality of nested objects. Inthe assessment creation interface 820. These selections can be receivedvia the creation interface 820, and/or can be received by the interfacemodule 852 of the processor 102 from the user device 106.

At block 866, the selected objects identified and/or data identifyingthe selected objects is stored. At block 868, the confirmation interface840 is generated. In some embodiments, the confirmation interface 840can include some or all of the features of the interface 840 shown inFIG. 15. In some embodiments, the confirmation interface 840 can begenerated by the server 102 and specifically by the interface module852. In some embodiments, the server 102, and/or the interface module852 can generate the confirmation interface 840 in response to receiptof selection of the first object and the second object in the creationinterface 820. In some embodiments, and as a part of the generation theconfirmation interface 840, the user can modify selection of one orseveral objects 828. In the confirmation interface 840. At block 870,the confirmation request is received, which confirmation requestcorresponds to manipulation by the user of the scheduling button 846.

After completion of some or all of steps 862 through 870 by theinterface module 852, the process 860 proceeds to steps performed by thegeneration module 854. At block 872 hierarchical data structure data isretrieved. In some embodiments, the hierarchical data structure data canbe retrieved from the database server 104, and specifically from thecontent library database 303. In some embodiments, hierarchical datastructure data can be retrieved at least for objects selected by theuser for inclusion in the assessment. While indicating is beingperformed by the generation module 854, in some embodiments, the step ofblock 872 can be performed by the interface module 852.

At block 874, one or several weighting values are identified. In someembodiments, these raw weighting values to be weighting values ofobjects selected by the user for inclusion in the assessment. Theseweighting values can be raw in that they have not yet been normalized toreflect weighting with respect to other objects identified for inclusionin the assessment. In some embodiments, the weighting value of an objectidentifies a relative contribution of the object to a level in thehierarchical data structure. In some embodiments, these weighting valuescan be identified from the hierarchical data structure data, and/or frommetadata associated with the selected objects. In embodiments in which afirst object and a second object are selected, a raw weighting value,can be retrieved for each of the first object in the second object. Theweighting values can be identified by the processor 102, andspecifically can be identified by the generation module 800 for based ondata retrieved from the database server 104.

A block 876, the identified raw weighting values are normalized viageneration of normalized weighting values. In some embodiments, thenormalized weighting value of an object identifies a relativecontribution of the object to a level in the hierarchical data structurecustomized based on object selection for inclusion in the assessment. Insome embodiments, the normalization of the raw weighting values caninclude identification of the relative contribution of the selectedobjects to the hierarchical data structure 800 and specifically to thesubset of the hierarchical data structure identified for inclusion inthe assessment. In some embodiments, normalized weighting values can begenerated for each of the first object and the second object selected bythe user. In some embodiments, generating the normalized weightingvalue, can include, for example, identifying a common level in thehierarchical structure shared by some or all of the selected objects. Insome embodiments, one or several categories of the common level can beupstream coupled to the selected objects and specifically to theselected first and second objects. In some embodiments, the normalizedvalue can be generated based on the identified common level, and/or onweighting levels associated with the selected objects and/or with thecommon level. The normalized weighting values can be generated by theserver 102 and specifically by the generation module 854.

At block 878, assessment parameter data is retrieved. The assessmentparameter data can be retrieved by the generation module 704 from thedatabase server 104 and specifically from the content library database303, and/or the evaluation database 308. In some embodiments, theassessment parameter data can specify, for example, a length of theassessment such as, for example, maximum number of items, and/or maximumtime for the assessment, one or several termination criteria for all orportions of the assessment, or the like.

Block 880 potential data packets and/or items for inclusion with thetest are identified. In some embodiments, this can include identifyingdata packets and/or items associated with the one or several objectsselected by the user. In some embodiments, these items can be identifiedbased on metadata associated with the items, and/or leasing the atomswith the object selected by the user. In some embodiments, for example,each object can be associated with one or several pointers pointing toitems associated with that object. In some embodiments, items identifiedin block 880 can be items of form an item bank, also referred to hereinas an item pool, of items that may be included in the assessment. Insome embodiment, the item bank includes more items than will bepresented in the assessment. The items can be identified from thecontent library database 303.

A block 882, the item pool is modified based on weighting values, andspecifically based on the normalized weighting values. In someembodiments, for example, this can include truncating number of items inthe item pool associated with one or several objects having a smallcontribution to the overall content of the assessment. The modifying ofthe data packet pool can be performed by the generation module 854. Atblock 884, an assessment blueprint is generated. The assessmentblueprint can be, in some embodiments, a configuration file used by theassessment algorithm to deliver the assessment. This assessmentblueprint identifies sources of data packets, termination criteria, andthe like. In some embodiments, the generation of the assessmentblueprint can include the linking of the item pool with, for example,the assessment parameter data, and information characterizing intendedrecipient of the assessment. In some embodiments, the generation of theassessment blueprint can further include identifying of content coveredby one or several previous assessments and the inclusion of a number ofitems relevant to this previously covered topics in the assessment. Theassessment blueprint can be generated by the generation module 854, andthe generated assessment can be stored in the database server 104 andspecifically in, for example, the content library database 303, and/orthe evaluation database 308.

At block 886, the assessment algorithm is launched. In some embodiments,the step of block 806 can be performed by the generation module 854, andin some embodiments, the step of block 886 can be performed by thedelivery module 856. The launch of the excessive algorithm can includethe receipt of requests from user for the assessment, the validating ofthe identity of the user, validating of one or several securitycriteria, or the like. Once the assessment algorithm has an launched,the process 860 proceeds to block 888, wherein the assessment isdelivered. In some embodiments, the assessment is delivered by theassessment algorithm launched in block 886 according to the assessmentblueprint generated in block 884. Details of assessment delivery will bediscussed at greater length below. However, assessment delivery caninclude selection of items for presentation from the item pool accordingto a user skill level estimate based on previously provided userresponses. Completion of delivery of the assessment can be determinedbased on the meeting of one or several termination criteria.

At block 890, the assessment is evaluated. In some embodiments, this caninclude evaluation of one or several received response, the generationof a user skill level relevant portions of the assessment, and/or thegeneration of a user skill level based on the entirety of theassessment. After the assessment has been evaluated, the process 860proceeds to block 892, wherein mastery of one or several contentcategories is determined. In some embodiments, the assessment can beevaluated according to a multidimensional model, which multidimensionalmodel can output an estimated user skill level, and/or estimated, usermastery level. The multidimensional model, can further allow, based on,for example, the estimated user skill level and/or user mastery level, adetermination of the user skill level, and/or mastery level relevant toother levels, and/or categories within the hierarchical data structure800. In some embodiments, the multidimensional model used to determinemastery of one or several content categories can eliminate multiplemastery calculations as previous assessment evaluation software's wereunable to simultaneously, via a multidimensional model, ascertain userskill level, and/or mastery level relevant across multiple levels of theportion the hierarchical data structure captured in the assessment. Insome embodiments, the determination of mastery can include determining askill level relevant content categories, comparing those one or severalskill levels to one or several thresholds indicative of mastery, anddetermining mastery based on that comparison. After the stand masteryhas been determined, the process 860 proceeds to block 894, wherein theuser profile is updated with the mastery of one or several contentcategories information determined in block 892. In some embodiments,this can include updating of the user profile database 301.

With reference now to FIG. 18, flowchart illustrating one embodiment ofprocess 900 for generating the normalized weighting value is shown. Theprocess 900 can be performed as a portion of, or in the place of thestep of block 876 of FIG. 17. The process 900 begins a block 902 whereinthe selected objects are identified. In some embodiments, the selectedobjects can be objects selected by the user in, for example, step 864 ofFIG. 17. After selected objects been identified, the process 900proceeds to block 904 wherein a common level in the hierarchical datastructures identified. In some embodiments, this can include identifyingthe lowest level in a hierarchical data structure in which the selectedobjects have a common category. Referring to FIG. 13, categories 804-Cand 804-D of a common level at the third level 802-C as both categories804-C and 804-D have the common category of 804-B in the third level802-C.

After the common level has been identified, the process 900 proceeds toblock 906, wherein contribution paths for the selected objects areidentified. In some embodiments, each selected object can have acontribution path, which contribution path links the selected object tothe common object, and/or the common category of the common levelidentified in block 904. For category 804-C, having a common category804-B with category 804-D, the contribution path of category 804-C isrepresented by arrow 806-B. In some embodiments, a contribution path canbe identified for each of the selected objects.

After the contribution paths have been identified, the process 900proceeds to block 908, wherein path weighting values are identified. Insome embodiments, the path weighting values are the weighting values ofeach of the categories along the contribution path between the selectedobject and the common object. By way of example, with the selectedobject 804-C, and a common object 804-A in a contribution path indicatedby arrows 806-A and 806-B, the path weighting values include theweighting value, 808 of category 804-B. In some embodiments, pathweighting values can be determined for each of the selected objects andasked for each of the identified contribution paths.

After the path weighting values have been determined, the process 900proceeds to block 910 wherein path weighting values are normalized. Insome embodiments, the path weighting values of a path can be normalizedby the multiplication of those weighting values. Thus, a normalized pathweighting value is the product of the weighting values of objects alongthe contribution path and between the selected object and the commonobject.

After the path weighting values to be normalized, the process 900proceeds to block 912, wherein object weighting values are normalized.The normalization of object weighting values can be a multi-step processthat includes, for each selected object, multiplying the weighting valueof that selected object by the normalized path weighting value of thatselected object to generate an object aggregate factor. An objectaggregate factor can be determined for each of the selected objects, andall of the object aggregate factors of the common category of the commonlevel can be added together to generate a modified weighting some. Theobject aggregate factor of each selected object can be divided by themodified weighting sum, the quotient of which division is the normalizedobject weighting value for that selected object. After normalized objectweighting values have been determined, the process 900 can proceed toblock 878 of FIG. 18.

With reference now to FIG. 19, a flowchart illustrating one embodimentof a process 920 for automated content selection and presentation, ormore specifically for automated assessment delivery and evaluation isshown. The process 920 can be performed by all or portions of the CDN100 including, for example, all or portions of the system 850. Theprocess 920 begins a block 922 wherein an item within a domain isselected. In some embodiments, this step can include receipt ofinformation from user, and specifically from the user device requestinginitiation of content delivery, and/or initiation of delivery of anassessment. Based on this received information, content for delivery, ormore specifically, an assessment can be selected and a first domainwithin the content for delivery, and more specifically, a first domainwithin the assessment can be selected. From this domain, a first itemcan be selected from the item pool associated with the domain. In someembodiments, the selection can be performed by the processor 102, andmore specifically by the recommendation engine of the contentcustomization system 402.

After the item has been selected, the process 920 proceeds to block 924,wherein the item is delivered. In some embodiments, the item, which canbe a question on a test, can be delivered by the processor 102 andspecifically by the delivery module 856. In some embodiments, thedelivery of the item can include the generation of an electronic messagecomprising the item and/or comprising a payload containing data to causea user interface on the user device 106 to deliver the item to the user.After the item has been delivered, the process 920 proceeds to block 926wherein a response is received. In some embodiments, the response can bereceived at the server 102 from the user device 106 and specifically atthe delivery module 856 of the server 102 from the user device 106.

After the responses been received, the process 920 proceeds to block 928wherein the response is evaluated. In some embodiments, the response canbe evaluated by the delivery module 856, and/or the evaluation system406. In some embodiments, the response can be evaluated by theevaluation engine of the evaluation system 406. In some embodiments, theresponse can be evaluated to determine the correctness, and/orincorrectness of the received response based on, for example,information contained content library database 303, and/or theevaluation data store 308.

After the response has been evaluated, the process 920 proceeds block930 wherein response vector is generated. In some embodiments, theresponse actor can characterize one or several attributes of thereceived response. In some embodiments, the response vector cancharacterize one or several attributes of the received responseincluding, for example, the result of the evaluation of the receivedresponses. The response vector can be generated by the delivery module856, and specifically by the evaluation system 406.

After the response vector has been generated, the process 920 proceedsto block 932, wherein the response vector is aligned with the domain. Insome embodiments, this can include the storing of the response vector.In the database server 104 and specifically in the user profile database301. The response vector can be stored in the database server 104 tolink the response vector with the item prompting the responsecharacterized by the response vector, and with the domain to which theitem belonged.

After the response vector is aligned with the domain coupled to the itemprompting receipt of the response giving rise to the response vector,the process 920 proceeds to block 934 wherein a domain score and/ordomain skill level is estimated. In some embodiments, the domain scoreand/or domain skill level can be estimated by identifying responsevectors relevant to the domain of the item selected and presented inblocks 922 and 924, and ingest the identified response vectors into thescoring engine, and specifically into a unidimensional model. Theunidimensional model can, in some embodiments, be trained to generate anestimated domain score and/or skill level for a single domain and/or canbe trained for use in generating domain scores and/or skill levels formultiple domains, which domain score and/or skill level is referred toherein as a scalar domain score and/or scalar skill level. In someembodiments, the estimating of the domain score and/or the domain skilllevel can further include the generation of a confidence level for thatestimated score. In some embodiments, the confidence level cancharacterize an estimated certainty of the accuracy of that score. Insome embodiments, the confidence level can be based on a number ofresponses received relevant to the selected domain, a consistency inresponses received for the selected domain, or the like

After the domain score and/or domain skill level has been estimated, theprocess 920 proceeds to block 936 wherein user data, and specificallywherein portions of the user profile database 301 is updated. In someembodiments, the user profile of the user from which the responsesreceived can be updated with the estimated domain score and/or domainskill level from block 934. In some embodiments, the domain score and/orthe domain skill level can be stored in the form of a domain vector inthe user profile database 300. One of the database server 104. After theuser data has been updated, the process 920 proceeds to decision step938, wherein does determined if domain termination criteria have beenmet and thus whether to terminate the providing of items from theselected domain. In embodiment in which the current domain of theassessment is the first domain selected as a part of step 922, theprocess 920 can determine whether termination criteria of the firstdomain have been met. In some embodiments, these criteria can include,for example, a maximum number of provided items, a maximum amount oftime spent on the domain, a maximum or minimum domain score and/ordomain skill level, a maximum or minimum confidence level, or the like.In some embodiments, termination criteria can be the same for alldomains, and in some embodiments, termination criteria can vary betweendomains. In some embodiments, it can be determined that domaintermination criteria are met when one or several of the domaintermination criteria are met, and/or when a desired percent of portionof the domain termination criteria are met. In some embodiments, thedetermination of step 938 can be made by the processor 102, the deliverymodule 856, the content customization system 402, and/or the contentdelivery system 408. If it is determined that the domain terminationcriteria are not met. In the process 920 returns to block 922, andselects a next item within the same domain as the previously selecteditem. In some embodiments, this next item can be selected based on thescalar skill level estimated in block 934. From block 922, the process920 proceeds as outlined above.

Due to this loop formed from block 922 through step 938, the process 920can deliver items within first content domain selected in block 922and/or in any other subsequently selected content domain untiltermination criteria for that content domain are met.

If it is determined that domain termination criteria are met, then theprocess proceeds to decision step 940, wherein it is determined ifassessment termination criteria are met. In some embodiments, assessmenttermination criteria can delineate between circumstances in which anassessment is to be terminated and circumstances in which an assessmentis to be continued. In some embodiments, these criteria can include, forexample, a maximum number of provided items, a maximum amount of timespent on the domain, a maximum or minimum domain score and/or domainskill level, a maximum or minimum confidence level, or the like. In someembodiments, it can be determined that assessment termination criteriaare met when one or several of the assessment termination criteria aremet, and/or when a desired percent of portion of the assessmenttermination criteria are met. In some embodiments, the determination ofstep 940 can be made by the processor 102, the delivery module 856, thecontent customization system 402, and/or the content delivery system408.

Due to this loop formed from block 922 through step 940, the process 920can deliver items within the assessment until termination criteria forthe assessment are met.

If it is determined that the assessment termination criteria have beenmet, and/or have inadequately met, than the process 920 proceeds toblock 942 wherein domains completed in the assessment are identifiedand/or wherein all of the domains completed by the user identified. Insome embodiments, this determination can be made based on a query and/oran evaluation of the user profile in the user profile database 301 ofthe database server 104. After completed domains been identified, domainskill levels, and/or domain scores for those completed domains areretrieved from the database server 104 and specifically from the userprofile database 301. As indicated in block 944. In some embodiments,this retrieving of domain scores and/or domain skill levels can includeretrieving domain vectors for those completed domains. In someembodiments, in addition to retrieving of domain scores and/or domainskill levels, response vectors for user responses received as part ofthe assessment can be retrieved, and/or response vectors for all pastreceived user responses can be retrieved.

At block 946, a vector estimated skill level can be generated with amultidimensional evaluation engine. In some embodiments, this caninclude launching a multidimensional evaluation engine when theassessment termination criteria are met and/or when at least oneassessment termination criteria is met. In some embodiments, this vectorestimated skill level can be at least partially redundant with scalarestimated skill levels for one or several of the domains estimated in,for example, block 934. In some embodiments, the skill level estimatedin block 946 can be multidimensional that can be, for example,characterized in a vector. In that it is based on multiple domainsand/or responses received across multiple domains, and in that it isrelevant to the common level of the assessment as well as to levelsalong contribution paths of content in the assessment. In someembodiments, the vector skill level can be based only on responsesreceived as part of the assessment, and in some embodiments, the vectorskill level can be generated based on responses received as part of theassessment as well as responses received before the assessment. In suchan embodiment in which the vector skill level is generated at least inpart based on responses received as part of the assessment as well as onresponses received before the assessment, the vector skill level cantrack a student's evolving mastery of one or several skills, standards,topics, or the like. The vector skill level can be generated with thedelivery module 706, with the server 102, and/or with the evaluationsystem 406.

After generating of the vector skill level, the process 920 proceeds toblock 948, wherein a proficiency level is determined and/or generated.In some embodiments, the proficiency level can be generated based on thevector skill level, and can comprise, the translation of the vectorskill level to a user meaningful scale. Thus, the proficiency level canbe a score readily understood by the user and/or more readily understoodby the user than the vector skill level. At block 950. The user profileof the user who completed the assessment is updated. In someembodiments, this user profile is updated with the user proficiencylevel generated in block 948, and/or with the vector skill levelgenerated in block 946.

Returning again to decision state 940, if it is determined that theassessment termination criteria have not been met, than the process 920proceeds to block 952 wherein domain vectors for completed domainswithin the assessment are retrieved. These domain vectors can beretrieved by the server 102 from the data base server 104 andspecifically from the user profile database 301 within the databaseserver 104. After the domain vectors are retrieved, process 920 proceedsto block 954 wherein a next domain is identified. In some embodiments,the next domain can be identified based on previously completed domains,based on the retrieved domain vectors indicating domain score and/ordomain skill levels, and/or based on a combination of previouslycompleted domains and/or the retrieved domain vectors. In someembodiments in which the domain identified as completed in step 938 wasthe first domain initially selected in block 922, the next domain can bea second content domain which can be selected when the terminationcriteria for the first content domain are met. The next domain can beidentified by the server 102, and specifically by the content deliverysystem 408, and/or the delivery module 856.

After the next domain is been identified, the process 920 proceeds toblock 956 wherein a next domain skill is estimated. In some embodiments,this next domain skill can be estimated based on estimated domain scoreand/or domain skill level of one or several previously completeddomains. In some embodiments, this next domain skill can be estimated byingesting the domain score and/or domain skill level of one or severalpreviously completed domains, which domain score(s) and/or domain skilllevel(s) can be in the form of one or several domain vectors, into astatistical learning model trained to estimate the domain level of anonstarter domain. In some embodiments in which the domain selected inblock 954 is the second content domain, step 956 can include estimatinga user skill level in the second content domain based, at least in part,on user responses received in the first content domain. Once the nextdomain skill level has been estimated, the process 920 returns to block922, and an item within the domain identified in block 954 is selectedaccording to the next domain skill level estimated in block 956. Inembodiments in which the domain identified in block 954 is the secondcontent domain, then the next item can be selected from items in thesecond content domain, and this next item can be selected based on theestimated user skill level in the second content domain. From block 922,the process 920 proceeds as outlined above.

A number of variations and modifications of the disclosed embodimentscan also be used. Specific details are given in the above description toprovide a thorough understanding of the embodiments. However, it isunderstood that the embodiments may be practiced without these specificdetails. For example, well-known circuits, processes, algorithms,structures, and techniques may be shown without unnecessary detail inorder to avoid obscuring the embodiments.

Implementation of the techniques, blocks, steps and means describedabove may be done in various ways. For example, these techniques,blocks, steps and means may be implemented in hardware, software, or acombination thereof. For a hardware implementation, the processing unitsmay be implemented within one or more application specific integratedcircuits (ASICs), digital signal processors (DSPs), digital signalprocessing devices (DSPDs), programmable logic devices (PLDs), fieldprogrammable gate arrays (FPGAs), processors, controllers,micro-controllers, microprocessors, other electronic units designed toperform the functions described above, and/or a combination thereof.

Also, it is noted that the embodiments may be described as a processwhich is depicted as a flowchart, a flow diagram, a swim diagram, a dataflow diagram, a structure diagram, or a block diagram. Although adepiction may describe the operations as a sequential process, many ofthe operations can be performed in parallel or concurrently. Inaddition, the order of the operations may be re-arranged. A process isterminated when its operations are completed, but could have additionalsteps not included in the figure. A process may correspond to a method,a function, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination corresponds to a return ofthe function to the calling function or the main function.

Furthermore, embodiments may be implemented by hardware, software,scripting languages, firmware, middleware, microcode, hardwaredescription languages, and/or any combination thereof. When implementedin software, firmware, middleware, scripting language, and/or microcode,the program code or code segments to perform the necessary tasks may bestored in a machine readable medium such as a storage medium. A codesegment or machine-executable instruction may represent a procedure, afunction, a subprogram, a program, a routine, a subroutine, a module, asoftware package, a script, a class, or any combination of instructions,data structures, and/or program statements. A code segment may becoupled to another code segment or a hardware circuit by passing and/orreceiving information, data, arguments, parameters, and/or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, etc.

For a firmware and/or software implementation, the methodologies may beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. Any machine-readable mediumtangibly embodying instructions may be used in implementing themethodologies described herein. For example, software codes may bestored in a memory. Memory may be implemented within the processor orexternal to the processor. As used herein the term “memory” refers toany type of long term, short term, volatile, nonvolatile, or otherstorage medium and is not to be limited to any particular type of memoryor number of memories, or type of media upon which memory is stored.

Moreover, as disclosed herein, the term “storage medium” may representone or more memories for storing data, including read only memory (ROM),random access memory (RAM), magnetic RAM, core memory, magnetic diskstorage mediums, optical storage mediums, flash memory devices and/orother machine readable mediums for storing information. The term“machine-readable medium” includes, but is not limited to portable orfixed storage devices, optical storage devices, and/or various otherstorage mediums capable of storing that contain or carry instruction(s)and/or data.

While the principles of the disclosure have been described above inconnection with specific apparatuses and methods, it is to be clearlyunderstood that this description is made only by way of example and notas limitation on the scope of the disclosure.

What is claimed is:
 1. A system for automated assessment generation, thesystem comprising: a memory comprising a content item library comprisinga hierarchical data structure having levels and a plurality of datapackets, each of which data packets is linked with at least a portion ofthe hierarchical data structure; and at least one server configured to:generate an assessment creation interface, the assessment creationinterface comprising a plurality of nested objects each representativeof a portion of the hierarchical data structure; receive a selection ofa first object and a second object from the plurality of nested objectsof the assessment creation interface; generate a weighting value foreach of the selected objects, wherein the weighting value of an objectidentifies a relative contribution of the object to a level in thehierarchical data structure; and generate an assessment from datapackets associated with the selected objects according to the weightingvalue.
 2. The system of claim 1, wherein the assessment creationinterface comprises: a first plurality of objects, each object of thefirst plurality of objects corresponding to one of a plurality ofdomains; a second plurality of objects, each object of which secondplurality of objects corresponding to one of a plurality of clusters;and a third plurality of objects, each object of which third pluralityof objects corresponding to one of a plurality of standards.
 3. Thesystem of claim 2, wherein each of the first plurality of objectscomprises a first-object boundary and contains at least one of thesecond plurality of objects nested within the first-object boundary. 4.The system of claim 3, wherein each of the second plurality of objectscomprises a second object boundary and contains at least one of thethird plurality of objects nested within the second object boundary. 5.The system of claim 4, wherein at least one of the first plurality ofobjects contains some of the second plurality of objects nested withinthe first-object boundary.
 6. The system of claim 5, wherein at leastone of the second plurality of objects contains some of the thirdplurality of objects nested within the second object boundary.
 7. Thesystem of claim 4, wherein the at least one server is further configuredto generate a confirmation interface in response to receipt of selectionof the first object and the second object.
 8. The system of claim 7,wherein each of the first object and the second object comprise one ofthe third plurality of objects.
 9. The system of claim 4, whereingenerating a weighting value comprises: retrieving a raw weighting valuefor each of the first object and the second object; and generating anormalized weighting value for each of the first object and the secondobject.
 10. The system of claim 9, wherein generating the normalizedweighting value comprises: identifying a common level in thehierarchical structure, wherein the common level comprises an objectupstream coupled with each of the first object and the second object;and wherein the normalized weighting value is generated based on thecommon level.
 11. A method for automated assessment generation, themethod comprising: generating an assessment creation interface, theassessment creation interface comprising a plurality of nested objectseach representative of a portion of a hierarchical data structurecomprising levels and a plurality of data packets, each of which datapackets is linked with at least a portion of the hierarchical datastructure; and receiving a selection of a first object and a secondobject from the plurality of nested objects of the assessment creationinterface; generating a weighting value for each of the selectedobjects, wherein the weighting value of an object identifies a relativecontribution of the object to a level in the hierarchical datastructure; and generating an assessment from data packets associatedwith the selected objects according to the weighting value.
 12. Themethod of claim 11, wherein the assessment creation interface comprises:a first plurality of objects, each object of the first plurality ofobjects corresponding to one of a plurality of domains; a secondplurality of objects, each object of which second plurality of objectscorresponding to one of a plurality of clusters; and a third pluralityof objects, each object of which third plurality of objectscorresponding to one of a plurality of standards.
 13. The method ofclaim 12, wherein each of the first plurality of objects comprises afirst-object boundary and contains at least one of the second pluralityof objects nested within the first-object boundary.
 14. The method ofclaim 13, wherein each of the second plurality of objects comprises asecond object boundary and contains at least one of the third pluralityof objects nested within the second object boundary.
 15. The method ofclaim 14, wherein at least one of the first plurality of objectscontains some of the second plurality of objects nested within thefirst-object boundary.
 16. The method of claim 15, wherein at least oneof the second objects contains some of the plurality of third objectsnested within the second object boundary.
 17. The method of claim 14,further comprising generating a confirmation interface in response toreceipt of selection of the first object and the second object andreceiving confirmation of selection of the first object and the secondobject.
 18. The method of claim 17, wherein each of the first object andthe second object comprise one of the third plurality of objects. 19.The method of claim 14, wherein generating a weighting value comprises:retrieving a raw weighting value for each of the first object and thesecond object; and generating a normalized weighting value for each ofthe first object and the second object.
 20. The method of claim 19,wherein generating the normalized weighting value comprises: identifyinga common level in the hierarchical structure, wherein the common levelcomprises an object upstream coupled with each of the first object andthe second object; and wherein the normalized weighting value isgenerated based on the common level.